{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d507e4e-dd7b-4784-88ea-e86963cad878",
   "metadata": {},
   "source": [
    "# Main script to run iGFA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ede1bcac-ac00-47ee-ae36-f909f6809a3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import warnings\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pyomo.environ as pyo\n",
    "from pyomo.contrib.iis import compute_infeasibility_explanation\n",
    "warnings.filterwarnings(\"ignore\", \"use_inf_as_na\")  # Generated by seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "253331f0-7183-4c3f-a25b-ea97c2de78d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cabsel/gfa/gfapy/model.py:3: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "sys.path.insert(0, '/home/cabsel/gfa/')\n",
    "from gfapy.model import summarize_instance\n",
    "from gfapy.plotter import plot_fig, plot_meas, plot_perturb\n",
    "from gfapy.igfa_fitflux import parse_model, res_plotter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba944354-ebf5-43c3-875d-7da0e0e9d8c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mainDir = '/home/cabsel/gfa/'\n",
    "inputDir = os.path.join(mainDir, 'inputfiles')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221d010c-0ce2-4d3b-9b2d-9d23986657a5",
   "metadata": {},
   "source": [
    "## Read GFA model from Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eccbaa86-5650-4562-b0c9-7cc7e8315d34",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>Ent_Man7</th>\n",
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       "<p>13 rows × 29 columns</p>\n",
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      "text/plain": [
       "            Ent_Man7  Ex_Man7  Ex_Man6  Ex_Man5  Ex_G0-GlcNAc  Ex_G0  Ex_G0F  \\\n",
       "Glycoforms                                                                     \n",
       "Man7             1.0     -1.0      0.0      0.0           0.0    0.0     0.0   \n",
       "Man6             0.0      0.0     -1.0      0.0           0.0    0.0     0.0   \n",
       "Man5             0.0      0.0      0.0     -1.0           0.0    0.0     0.0   \n",
       "G0-GlcNAc        0.0      0.0      0.0      0.0          -1.0    0.0     0.0   \n",
       "G0               0.0      0.0      0.0      0.0           0.0   -1.0     0.0   \n",
       "G0F              0.0      0.0      0.0      0.0           0.0    0.0    -1.0   \n",
       "G0F-GlcNAc       0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "G0F+GlcNAc       0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "G1a/b            0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "G1Fa/b           0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "G2F              0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "A1G1F            0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "A1G2F            0.0      0.0      0.0      0.0           0.0    0.0     0.0   \n",
       "\n",
       "            Ex_G0F-GlcNAc  Ex_G0F+GlcNAc  Ex_G1a/b  ...  R8  R6  R7  R9 R10  \\\n",
       "Glycoforms                                          ...                       \n",
       "Man7                  0.0            0.0       0.0  ...   0   0   0   0   0   \n",
       "Man6                  0.0            0.0       0.0  ...   0   0   0   0   0   \n",
       "Man5                  0.0            0.0       0.0  ...   0   0   0   0   0   \n",
       "G0-GlcNAc             0.0            0.0       0.0  ...   0   0  -1   0   0   \n",
       "G0                    0.0            0.0       0.0  ...   0  -1   0   0  -1   \n",
       "G0F                   0.0            0.0       0.0  ...  -1   1   0   0   0   \n",
       "G0F-GlcNAc           -1.0            0.0       0.0  ...   0   0   1   0   0   \n",
       "G0F+GlcNAc            0.0           -1.0       0.0  ...   1   0   0   0   0   \n",
       "G1a/b                 0.0            0.0      -1.0  ...   0   0   0  -1   1   \n",
       "G1Fa/b                0.0            0.0       0.0  ...   0   0   0   1   0   \n",
       "G2F                   0.0            0.0       0.0  ...   0   0   0   0   0   \n",
       "A1G1F                 0.0            0.0       0.0  ...   0   0   0   0   0   \n",
       "A1G2F                 0.0            0.0       0.0  ...   0   0   0   0   0   \n",
       "\n",
       "           R11 R12 R13 R14 R15  \n",
       "Glycoforms                      \n",
       "Man7         0   0   0   0   0  \n",
       "Man6         0   0   0   0   0  \n",
       "Man5         0   0   0   0   0  \n",
       "G0-GlcNAc    0   0   0   0   0  \n",
       "G0           0   0   0   0   0  \n",
       "G0F         -1   0   0   0   0  \n",
       "G0F-GlcNAc   0   0   0   0   0  \n",
       "G0F+GlcNAc   0   0   0   0   0  \n",
       "G1a/b        0   0   0   0   0  \n",
       "G1Fa/b       1  -1   0  -1   0  \n",
       "G2F          0   1   0   0  -1  \n",
       "A1G1F        0   0  -1   1   0  \n",
       "A1G2F        0   0   1   0   1  \n",
       "\n",
       "[13 rows x 29 columns]"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Compartments</th>\n",
       "      <th>Structure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycoforms</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Man7</th>\n",
       "      <td>cis_golgi</td>\n",
       "      <td>{Man(1-?)}{Man(2-?)}Man(?)[Man(?)]Man(?)[Man(?...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man6</th>\n",
       "      <td>cis_golgi</td>\n",
       "      <td>{Man(1-?)}Man(?)[Man(?)]Man(?)[Man(?)]Man(?)Gl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Man5</th>\n",
       "      <td>cis_golgi</td>\n",
       "      <td>Man(?)[Man(?)]Man(?)[Man(?)]Man(?)GlcNAc(?)GlcNAc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G0-GlcNAc</th>\n",
       "      <td>medial_golgi</td>\n",
       "      <td>Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?)GlcNAc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G0</th>\n",
       "      <td>medial_golgi</td>\n",
       "      <td>GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G0F</th>\n",
       "      <td>medial_golgi</td>\n",
       "      <td>GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G0F-GlcNAc</th>\n",
       "      <td>medial_golgi</td>\n",
       "      <td>Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?)[Fuc(?)]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G0F+GlcNAc</th>\n",
       "      <td>medial_golgi</td>\n",
       "      <td>GlcNAc(?)[GlcNAc(?)]Man(?)[GlcNAc(?)Man(?)]Man...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1a/b</th>\n",
       "      <td>trans Golgi</td>\n",
       "      <td>{Gal(1-?)}GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1Fa/b</th>\n",
       "      <td>trans Golgi</td>\n",
       "      <td>{Gal(1-?)}GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2F</th>\n",
       "      <td>trans Golgi</td>\n",
       "      <td>Gal(?)GlcNAc(?)Man(?)[Gal(?)GlcNAc(?)Man(?)]Ma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1G1F</th>\n",
       "      <td>trans Golgi</td>\n",
       "      <td>{Neu5Ac(1-?)Gal()}GlcNAc(?)Man(?)[GlcNAc(?)Man...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1G2F</th>\n",
       "      <td>trans Golgi</td>\n",
       "      <td>{Neu5Ac(1-?)}Gal(?)GlcNAc(?)Man(?)[Gal(?)GlcNA...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Compartments                                          Structure\n",
       "Glycoforms                                                                 \n",
       "Man7           cis_golgi  {Man(1-?)}{Man(2-?)}Man(?)[Man(?)]Man(?)[Man(?...\n",
       "Man6           cis_golgi  {Man(1-?)}Man(?)[Man(?)]Man(?)[Man(?)]Man(?)Gl...\n",
       "Man5           cis_golgi  Man(?)[Man(?)]Man(?)[Man(?)]Man(?)GlcNAc(?)GlcNAc\n",
       "G0-GlcNAc   medial_golgi       Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?)GlcNAc\n",
       "G0          medial_golgi  GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?...\n",
       "G0F         medial_golgi  GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?...\n",
       "G0F-GlcNAc  medial_golgi  Man(?)[GlcNAc(?)Man(?)]Man(?)GlcNAc(?)[Fuc(?)]...\n",
       "G0F+GlcNAc  medial_golgi  GlcNAc(?)[GlcNAc(?)]Man(?)[GlcNAc(?)Man(?)]Man...\n",
       "G1a/b        trans Golgi  {Gal(1-?)}GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(...\n",
       "G1Fa/b       trans Golgi  {Gal(1-?)}GlcNAc(?)Man(?)[GlcNAc(?)Man(?)]Man(...\n",
       "G2F          trans Golgi  Gal(?)GlcNAc(?)Man(?)[Gal(?)GlcNAc(?)Man(?)]Ma...\n",
       "A1G1F        trans Golgi  {Neu5Ac(1-?)Gal()}GlcNAc(?)Man(?)[GlcNAc(?)Man...\n",
       "A1G2F        trans Golgi  {Neu5Ac(1-?)}Gal(?)GlcNAc(?)Man(?)[Gal(?)GlcNA..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reaction Description</th>\n",
       "      <th>Enzymes</th>\n",
       "      <th>Enzyme Name</th>\n",
       "      <th>Notes</th>\n",
       "      <th>Genes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reaction ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ent_Man7</th>\n",
       "      <td>Entry of Man7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_Man7</th>\n",
       "      <td>Secretion of Man7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_Man6</th>\n",
       "      <td>Secretion of Man6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_Man5</th>\n",
       "      <td>Secretion of Man5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G0-GlcNAc</th>\n",
       "      <td>Secretion of G0-GlcNAc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G0</th>\n",
       "      <td>Secretion of G0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G0F</th>\n",
       "      <td>Secretion of G0F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G0F-GlcNAc</th>\n",
       "      <td>Secretion of G0F-GlcNAc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G0F+GlcNAc</th>\n",
       "      <td>Secretion of G0F+GlcNAc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G1a/b</th>\n",
       "      <td>Secretion of G1a/b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G1Fa/b</th>\n",
       "      <td>Secretion of G1Fa/b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_G2F</th>\n",
       "      <td>Secretion of G2F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_A1G1F</th>\n",
       "      <td>Secretion of A1G1F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ex_A1G2F</th>\n",
       "      <td>Secretion of A1G2F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>Man7 trimmed to Man6</td>\n",
       "      <td>ManI</td>\n",
       "      <td>Mannosidase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MAN1A1, MAN1A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>Man 6 trimmed to Man5</td>\n",
       "      <td>ManI</td>\n",
       "      <td>Mannosidase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MAN1A1, MAN1A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R3</th>\n",
       "      <td>Man5 undergoes trimming and GlcNAc addition to...</td>\n",
       "      <td>GnTI/ManII</td>\n",
       "      <td>Multiple enzymes</td>\n",
       "      <td>MGAT1 followed by MAN2A1/MAN2A2</td>\n",
       "      <td>MGAT1, MAN2A1, MAN2A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R4</th>\n",
       "      <td>GlcNAc addition to form G0</td>\n",
       "      <td>GnTII</td>\n",
       "      <td>Glucosaminyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGAT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R5</th>\n",
       "      <td>GlcNAc addition to form G0F</td>\n",
       "      <td>GnTII</td>\n",
       "      <td>Glucosaminyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGAT2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R8</th>\n",
       "      <td>GlcNAc addition to form G0F+GlcNAc</td>\n",
       "      <td>GnTIV</td>\n",
       "      <td>Glucosaminyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MGAT4A, MGAT4B, MGAT5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R6</th>\n",
       "      <td>Fucosylation of N-glycans</td>\n",
       "      <td>FucT</td>\n",
       "      <td>Fucosyltransferase</td>\n",
       "      <td>FUT10, FUT11 in invertebrates/plants</td>\n",
       "      <td>FUT8, FUT10, FUT11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R7</th>\n",
       "      <td>Fucosylation of N-glycans</td>\n",
       "      <td>FucT</td>\n",
       "      <td>Fucosyltransferase</td>\n",
       "      <td>FUT10, FUT11 in invertebrates/plants</td>\n",
       "      <td>FUT8, FUT10, FUT11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R9</th>\n",
       "      <td>Fucosylation of N-glycans</td>\n",
       "      <td>FucT</td>\n",
       "      <td>Fucosyltransferase</td>\n",
       "      <td>FUT10, FUT11 in invertebrates/plants</td>\n",
       "      <td>FUT8, FUT10, FUT11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R10</th>\n",
       "      <td>Galactose addition</td>\n",
       "      <td>GalT</td>\n",
       "      <td>Galactosyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R11</th>\n",
       "      <td>Galactose addition</td>\n",
       "      <td>GalT</td>\n",
       "      <td>Galactosyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R12</th>\n",
       "      <td>Galactose addition</td>\n",
       "      <td>GalT</td>\n",
       "      <td>Galactosyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R13</th>\n",
       "      <td>Galactose addition</td>\n",
       "      <td>GalT</td>\n",
       "      <td>Galactosyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R14</th>\n",
       "      <td>Neu5Ac addition</td>\n",
       "      <td>SiaT</td>\n",
       "      <td>Sialyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST3GAL3, ST3GAL4, ST3GAL6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R15</th>\n",
       "      <td>Neu5Ac addition</td>\n",
       "      <td>SiaT</td>\n",
       "      <td>Sialyltransferase</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST3GAL3, ST3GAL4, ST3GAL6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Reaction Description     Enzymes  \\\n",
       "Reaction ID                                                                    \n",
       "Ent_Man7                                           Entry of Man7         NaN   \n",
       "Ex_Man7                                        Secretion of Man7         NaN   \n",
       "Ex_Man6                                        Secretion of Man6         NaN   \n",
       "Ex_Man5                                        Secretion of Man5         NaN   \n",
       "Ex_G0-GlcNAc                              Secretion of G0-GlcNAc         NaN   \n",
       "Ex_G0                                            Secretion of G0         NaN   \n",
       "Ex_G0F                                          Secretion of G0F         NaN   \n",
       "Ex_G0F-GlcNAc                            Secretion of G0F-GlcNAc         NaN   \n",
       "Ex_G0F+GlcNAc                            Secretion of G0F+GlcNAc         NaN   \n",
       "Ex_G1a/b                                      Secretion of G1a/b         NaN   \n",
       "Ex_G1Fa/b                                    Secretion of G1Fa/b         NaN   \n",
       "Ex_G2F                                          Secretion of G2F         NaN   \n",
       "Ex_A1G1F                                      Secretion of A1G1F         NaN   \n",
       "Ex_A1G2F                                      Secretion of A1G2F         NaN   \n",
       "R1                                          Man7 trimmed to Man6        ManI   \n",
       "R2                                         Man 6 trimmed to Man5        ManI   \n",
       "R3             Man5 undergoes trimming and GlcNAc addition to...  GnTI/ManII   \n",
       "R4                                    GlcNAc addition to form G0       GnTII   \n",
       "R5                                   GlcNAc addition to form G0F       GnTII   \n",
       "R8                            GlcNAc addition to form G0F+GlcNAc       GnTIV   \n",
       "R6                                     Fucosylation of N-glycans        FucT   \n",
       "R7                                     Fucosylation of N-glycans        FucT   \n",
       "R9                                     Fucosylation of N-glycans        FucT   \n",
       "R10                                          Galactose addition         GalT   \n",
       "R11                                          Galactose addition         GalT   \n",
       "R12                                          Galactose addition         GalT   \n",
       "R13                                          Galactose addition         GalT   \n",
       "R14                                              Neu5Ac addition        SiaT   \n",
       "R15                                              Neu5Ac addition        SiaT   \n",
       "\n",
       "                           Enzyme Name                                 Notes  \\\n",
       "Reaction ID                                                                    \n",
       "Ent_Man7                           NaN                                   NaN   \n",
       "Ex_Man7                            NaN                                   NaN   \n",
       "Ex_Man6                            NaN                                   NaN   \n",
       "Ex_Man5                            NaN                                   NaN   \n",
       "Ex_G0-GlcNAc                       NaN                                   NaN   \n",
       "Ex_G0                              NaN                                   NaN   \n",
       "Ex_G0F                             NaN                                   NaN   \n",
       "Ex_G0F-GlcNAc                      NaN                                   NaN   \n",
       "Ex_G0F+GlcNAc                      NaN                                   NaN   \n",
       "Ex_G1a/b                           NaN                                   NaN   \n",
       "Ex_G1Fa/b                          NaN                                   NaN   \n",
       "Ex_G2F                             NaN                                   NaN   \n",
       "Ex_A1G1F                           NaN                                   NaN   \n",
       "Ex_A1G2F                           NaN                                   NaN   \n",
       "R1                         Mannosidase                                   NaN   \n",
       "R2                         Mannosidase                                   NaN   \n",
       "R3                    Multiple enzymes       MGAT1 followed by MAN2A1/MAN2A2   \n",
       "R4             Glucosaminyltransferase                                   NaN   \n",
       "R5             Glucosaminyltransferase                                   NaN   \n",
       "R8             Glucosaminyltransferase                                   NaN   \n",
       "R6                  Fucosyltransferase  FUT10, FUT11 in invertebrates/plants   \n",
       "R7                  Fucosyltransferase  FUT10, FUT11 in invertebrates/plants   \n",
       "R9                  Fucosyltransferase  FUT10, FUT11 in invertebrates/plants   \n",
       "R10              Galactosyltransferase                                   NaN   \n",
       "R11              Galactosyltransferase                                   NaN   \n",
       "R12              Galactosyltransferase                                   NaN   \n",
       "R13              Galactosyltransferase                                   NaN   \n",
       "R14                  Sialyltransferase                                   NaN   \n",
       "R15                  Sialyltransferase                                   NaN   \n",
       "\n",
       "                                                           Genes  \n",
       "Reaction ID                                                       \n",
       "Ent_Man7                                                     NaN  \n",
       "Ex_Man7                                                      NaN  \n",
       "Ex_Man6                                                      NaN  \n",
       "Ex_Man5                                                      NaN  \n",
       "Ex_G0-GlcNAc                                                 NaN  \n",
       "Ex_G0                                                        NaN  \n",
       "Ex_G0F                                                       NaN  \n",
       "Ex_G0F-GlcNAc                                                NaN  \n",
       "Ex_G0F+GlcNAc                                                NaN  \n",
       "Ex_G1a/b                                                     NaN  \n",
       "Ex_G1Fa/b                                                    NaN  \n",
       "Ex_G2F                                                       NaN  \n",
       "Ex_A1G1F                                                     NaN  \n",
       "Ex_A1G2F                                                     NaN  \n",
       "R1                                                MAN1A1, MAN1A2  \n",
       "R2                                                MAN1A1, MAN1A2  \n",
       "R3                                         MGAT1, MAN2A1, MAN2A2  \n",
       "R4                                                         MGAT2  \n",
       "R5                                                         MGAT2  \n",
       "R8                                         MGAT4A, MGAT4B, MGAT5  \n",
       "R6                                            FUT8, FUT10, FUT11  \n",
       "R7                                            FUT8, FUT10, FUT11  \n",
       "R9                                            FUT8, FUT10, FUT11  \n",
       "R10            B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...  \n",
       "R11            B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...  \n",
       "R12            B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...  \n",
       "R13            B4GALT1, B4GALT2, B4GALT3, B4GALT4, B4GALT5, B...  \n",
       "R14                                    ST3GAL3, ST3GAL4, ST3GAL6  \n",
       "R15                                    ST3GAL3, ST3GAL4, ST3GAL6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rxn_models = pd.read_excel(os.path.join(inputDir, 'Lee et al', 'Leeetal_model.xlsx'), sheet_name=None)\n",
    "stoich_data = rxn_models['Stoichiometry'].iloc[1:, :].set_index(\"Glycoforms\")\n",
    "rxn_meta = rxn_models['Enzyme-Reaction Relations'].set_index(\"Reaction ID\")\n",
    "\n",
    "met_meta_cols = sorted(set(stoich_data.columns) - set(rxn_meta.index))\n",
    "met_meta = stoich_data[met_meta_cols].copy()\n",
    "stoich_data = stoich_data.drop(columns=met_meta_cols)\n",
    "\n",
    "display(stoich_data)\n",
    "display(met_meta)\n",
    "display(rxn_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ca4e33-6d1e-446f-85a0-4e091cb9e0fd",
   "metadata": {},
   "source": [
    "## Read and format data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "faf14cbb-9667-4d1d-bfba-2a87d4b19a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "pH_scale = 'highpH'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "712066fc-572f-407a-a2d2-62ef626659ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "expt_data = pd.read_excel(os.path.join(inputDir, 'Lee et al', pH_scale+'_processed.xlsx'), sheet_name=None, index_col=0)\n",
    "expt_data['q_prod'].loc[13, :] = expt_data['q_prod'].loc[12, :]\n",
    "expt_data['q_prod'] = expt_data['q_prod'].drop(index=[12, 14])\n",
    "expt_data['Fractions'].loc[13, :] = expt_data['Fractions'].loc[[12, 14], :].mean()\n",
    "expt_data['Fractions'] = expt_data['Fractions'].drop(index=[12, 14])\n",
    "expt_data['fit_Fractions'] = expt_data['fit_Concentrations'].div(expt_data['Titer']['Titer (g/L)'], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39610511-abac-48db-b472-d1f32790721b",
   "metadata": {},
   "source": [
    "<b> Comments: </b> Multiply titer with 1000 for unit consistency \n",
    "$$\n",
    "v_{E, i}(t) = \\frac{1}{VCD(t)} \\cdot \\frac{dfrac_{i}(t) \\cdot C_{mAb}(t)}{dt} + frac_{i}(t) \\cdot \\left (Q_{prod}(t) - \\frac{dC_{mAB}(t)}{dt} \\cdot \\frac{1}{VCD}\\right)\\\\\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9609278-52a5-4853-91a3-26b62cd0df43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['VCD', 'Titer', 'Fractions', 'q_prod', 'Concentrations', 'fit_Concentrations', 'diff_Concentrations', 'fit_Fractions', 'Secreted Flux'])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dtiter_VCD = (expt_data['Titer']['diff_Titer (g/L)'].div(expt_data['VCD']['VCD (1E6 VC/mL)']))*1000\n",
    "secondterm = (expt_data['Fractions'].\n",
    "              mul(expt_data['q_prod']['Spec Prod (pg/cells/day)'] - dtiter_VCD[expt_data['q_prod'].index], axis=0).\n",
    "              dropna())\n",
    "dC_VCD = (expt_data['diff_Concentrations'].div(expt_data['VCD']['VCD (1E6 VC/mL)'], axis=0).dropna())*1000\n",
    "expt_data['Secreted Flux'] = dC_VCD.loc[expt_data['q_prod'].index, :] + secondterm\n",
    "display(expt_data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dee39202-8f13-4413-b29e-e505d9a99343",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cabsel/miniconda3/envs/Shri/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1185: RuntimeWarning: All-NaN axis encountered\n",
      "  miny = np.nanmin(masked_verts[..., 1])\n",
      "/home/cabsel/miniconda3/envs/Shri/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1186: RuntimeWarning: All-NaN axis encountered\n",
      "  maxy = np.nanmax(masked_verts[..., 1])\n",
      "/home/cabsel/miniconda3/envs/Shri/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1185: RuntimeWarning: All-NaN axis encountered\n",
      "  miny = np.nanmin(masked_verts[..., 1])\n",
      "/home/cabsel/miniconda3/envs/Shri/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1186: RuntimeWarning: All-NaN axis encountered\n",
      "  maxy = np.nanmax(masked_verts[..., 1])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
    "sns.barplot(data=expt_data['q_prod'], x='Time window (days)', y='Spec Prod (pg/cells/day)', ax=ax[0], facecolor='#F7F6F6', edgecolor='k', hatch='/', fill=True)\n",
    "ax[0].set_title('Specific Productivity')\n",
    "plot_fig(time_col='Time (WD)', data=expt_data['Titer'], meas_col='Titer (g/L)', smooth_col='fit_Titer (g/L)', ax=ax[1])\n",
    "ax[1].set_title('Titer')\n",
    "plot_fig(time_col='Time (WD)', data=expt_data['VCD'], meas_col='VCD (1E6 VC/mL)', smooth_col='fit_VCD (1E6 VC/mL)', ax=ax[2])\n",
    "ax[2].set_title('VCD')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c9af19-39ec-46b4-8adf-450f0e7f715f",
   "metadata": {},
   "source": [
    "## Initialize iGFA and ingest experimental measurements into iGFA's class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "108b6792-5fd8-4a98-b980-9125fcf80845",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_rxns = ['R1', 'R2', 'R3']  # 'R1', 'R2', 'R3', 'R4', 'R5', 'R8', 'R6', 'R7', 'R9', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15'\n",
    "init_model = parse_model(stoich_data, rxn_meta, met_meta, 1, linear_rxns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e186912-c5c5-47d0-adba-10eaa628ff18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['timepoints', 'timepoints_data', 'spec_prod', 'secreted_flux_data'])\n"
     ]
    }
   ],
   "source": [
    "init_model.read_measurements(expt_data, time_col_name='Time (WD)')\n",
    "data = init_model.fetch_data(spec_prod_col='Spec Prod (pg/cells/day)') \n",
    "# Use get_diff=True only when derivative of fractions is precalculated\n",
    "print(data[None].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6eb01b9-031a-456c-bc6e-3b79fc2fc05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[None]['reg_param'] = {None: 100}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3618a688-9221-4f94-ba49-b96e21e04b5b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number_of_constraints: 53\n",
      "number_of_objectives: 1\n",
      "number_of_variables: 84\n",
      "\n",
      "\n",
      "Names of variables in model:\n",
      "entry_flux : 4 variables\n",
      "lin_flux : 12 variables\n",
      "v_ref : 12 variables\n",
      "alpha : 20 variables\n",
      "beta : 32 variables\n",
      "gamma : 4 variables\n",
      "\n",
      "\n",
      "Names of constraints in model:\n",
      "pos_secretions : 52 constraints\n",
      "min_params : 1 constraints\n",
      "\n",
      "\n",
      "Names of objectives in model:\n",
      "obj : 1 objective\n",
      "\n",
      "\n",
      "timepoints : Size=1, Index=None, Ordered=Sorted\n",
      "    Key  : Dimen : Domain : Size : Members\n",
      "    None :     1 :    Any :    4 : {5, 8, 10, 13}\n",
      "None\n",
      "mets : Size=1, Index=None, Ordered=Insertion\n",
      "    Key  : Dimen : Domain : Size : Members\n",
      "    None :     1 :    Any :   13 : {'Man7', 'Man6', 'Man5', 'G0-GlcNAc', 'G0', 'G0F', 'G0F-GlcNAc', 'G0F+GlcNAc', 'G1a/b', 'G1Fa/b', 'G2F', 'A1G1F', 'A1G2F'}\n",
      "None\n",
      "enzymes : Size=1, Index=None, Ordered=Insertion\n",
      "    Key  : Dimen : Domain : Size : Members\n",
      "    None :     1 :    Any :    5 : {'FucT', 'GalT', 'GnTII', 'GnTIV', 'SiaT'}\n",
      "None\n",
      "internal_rxns : Size=1, Index=None, Ordered=Insertion\n",
      "    Key  : Dimen : Domain : Size : Members\n",
      "    None :     1 :    Any :   15 : {'R1', 'R2', 'R3', 'R4', 'R5', 'R8', 'R6', 'R7', 'R9', 'R10', 'R11', 'R12', 'R13', 'R14', 'R15'}\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = init_model.create_pyomomodel(fit_beta=True, regularize_params=True)  # Use precalc_diff only when derivative of fractions is precalculated\n",
    "instance = model.create_instance(data=data)\n",
    "# discretizer = pyo.TransformationFactory('dae.finite_difference')\n",
    "# discretizer.apply_to(instance, nfe=3, wrt=model.timepoints, scheme='CENTRAL')\n",
    "summarize_instance(instance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe6eddf-e26a-49e6-aef2-e240de42c8e4",
   "metadata": {},
   "source": [
    "## Single start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fe2a589-7d6f-47d1-81eb-daa704ea9349",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# result = init_model.run_singlestart(instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2ec3cf1-3668-47bd-9c47-3d3a6a79adba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# solver = pyo.SolverFactory('ipopt')\n",
    "# compute_infeasibility_explanation(instance, solver)\n",
    "# pyo.value(instance.alpha[5, 'FucT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee630e2-ebd8-430d-8c63-a7f444a7cd8f",
   "metadata": {},
   "source": [
    "## Multi start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5cbcc73c-5f62-4ad5-b79d-d27f07184fb5",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b66c975bc6a74b1e9c0b0f422d5b4dc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Progress:   0%|          | 0/100 [00:00<?]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 2944\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.2713811175031994e+02    1.2713811175031994e+02\n",
      "Dual infeasibility......:   1.0045770297741345e+04    1.0045770297741345e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   5.4383436271719260e-09    5.4383436271719260e-09\n",
      "Overall NLP error.......:   1.0045770297741345e+04    1.0045770297741345e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 3324\n",
      "Number of objective gradient evaluations             = 2927\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 3325\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 2948\n",
      "Number of Lagrangian Hessian evaluations             = 2945\n",
      "Total seconds in IPOPT                               = 2.883\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 2.9024105072021484\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "0 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 287\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.2650053535286864e-06    3.0586169934289501e+02\n",
      "Dual infeasibility......:   6.1822580109221386e-04    3.5914796168812070e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   5.8043850000271039e-09    3.3719605974547406e-01\n",
      "Overall NLP error.......:   6.1822580109221386e-04    3.5914796168812070e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 490\n",
      "Number of objective gradient evaluations             = 266\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 491\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 290\n",
      "Number of Lagrangian Hessian evaluations             = 288\n",
      "Total seconds in IPOPT                               = 0.299\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.3244059085845947\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "1 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 295\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.0227263766094073e-06    5.0227263766094075e+02\n",
      "Dual infeasibility......:   4.7427266691903603e-04    4.7427266691903606e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.6218446681496190e-08    2.6218446681496190e+00\n",
      "Overall NLP error.......:   4.7427266691903603e-04    4.7427266691903606e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 617\n",
      "Number of objective gradient evaluations             = 284\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 617\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 298\n",
      "Number of Lagrangian Hessian evaluations             = 296\n",
      "Total seconds in IPOPT                               = 0.316\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.3396930694580078\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "2 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 352\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7431826692052686e-09    2.7431826692052685e-01\n",
      "Dual infeasibility......:   1.4891825055003255e-09    1.0157870832573417e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544316382654648e-13    1.2544316382654647e-05\n",
      "Overall NLP error.......:   1.4891825055003255e-09    1.0157870832573417e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 827\n",
      "Number of objective gradient evaluations             = 353\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 827\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 353\n",
      "Number of Lagrangian Hessian evaluations             = 352\n",
      "Total seconds in IPOPT                               = 0.384\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4102442264556885\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "3 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 265\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.9533848682690785e-06    3.9533848682690785e+02\n",
      "Dual infeasibility......:   4.3931300235687038e-04    4.3931300235687035e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3337614452399724e-08    1.3337614452399724e+00\n",
      "Overall NLP error.......:   4.3931300235687038e-04    4.3931300235687035e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 392\n",
      "Number of objective gradient evaluations             = 246\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 392\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 268\n",
      "Number of Lagrangian Hessian evaluations             = 266\n",
      "Total seconds in IPOPT                               = 0.278\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.30240654945373535\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "4 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 410\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.1879893892098237e-09    2.1879893892098237e-01\n",
      "Dual infeasibility......:   1.3191538075597193e-09    1.3191538075597192e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2605007239071396e-13    1.2605007239071397e-05\n",
      "Overall NLP error.......:   1.3191538075597193e-09    1.3191538075597192e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1086\n",
      "Number of objective gradient evaluations             = 411\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1086\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 411\n",
      "Number of Lagrangian Hessian evaluations             = 410\n",
      "Total seconds in IPOPT                               = 0.453\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4784533977508545\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "5 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 346\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7908617507694593e-09    2.7908617507694594e-01\n",
      "Dual infeasibility......:   3.9531713162050397e-09    3.9531713162050397e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544306559264640e-13    1.2544306559264639e-05\n",
      "Overall NLP error.......:   3.9531713162050397e-09    3.9531713162050397e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 913\n",
      "Number of objective gradient evaluations             = 347\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 913\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 347\n",
      "Number of Lagrangian Hessian evaluations             = 346\n",
      "Total seconds in IPOPT                               = 0.380\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4054388999938965\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "6 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 449\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.0575178846231671e-09    3.0575178846231671e-01\n",
      "Dual infeasibility......:   7.0737257025011241e-09    7.0737257025011246e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544396082305815e-13    1.2544396082305814e-05\n",
      "Overall NLP error.......:   7.0737257025011241e-09    7.0737257025011246e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1923\n",
      "Number of objective gradient evaluations             = 450\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1924\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 451\n",
      "Number of Lagrangian Hessian evaluations             = 449\n",
      "Total seconds in IPOPT                               = 0.502\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5270059108734131\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "7 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 291\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.6425808392227948e-09    3.6425808392227949e-01\n",
      "Dual infeasibility......:   9.6216743142754520e-09    4.7162400338321114e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   4.0847371771990124e-13    4.0847371771990123e-05\n",
      "Overall NLP error.......:   9.6216743142754520e-09    4.7162400338321114e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 468\n",
      "Number of objective gradient evaluations             = 292\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 468\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 292\n",
      "Number of Lagrangian Hessian evaluations             = 291\n",
      "Total seconds in IPOPT                               = 0.306\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3310372829437256\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "8 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 353\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.1033789832574040e-09    5.1033789832574039e-01\n",
      "Dual infeasibility......:   1.4858879859329830e-09    1.4858879859329829e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   7.9023617131321722e-13    7.9023617131321718e-05\n",
      "Overall NLP error.......:   1.4858879859329830e-09    1.4858879859329829e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 582\n",
      "Number of objective gradient evaluations             = 354\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 582\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 354\n",
      "Number of Lagrangian Hessian evaluations             = 353\n",
      "Total seconds in IPOPT                               = 0.383\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.40851521492004395\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "9 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 307\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.6138805452850772e-09    3.6138805452850770e-01\n",
      "Dual infeasibility......:   6.9786860750507428e-07    6.9786860750507429e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   8.3769843258257046e-13    8.3769843258257049e-05\n",
      "Overall NLP error.......:   6.9786860750507428e-07    6.9786860750507429e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 609\n",
      "Number of objective gradient evaluations             = 309\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 609\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 309\n",
      "Number of Lagrangian Hessian evaluations             = 308\n",
      "Total seconds in IPOPT                               = 0.345\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.36908507347106934\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "10 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 373\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   7.1106613208543808e-06    7.1106613208543808e+02\n",
      "Dual infeasibility......:   6.0958111751187349e-04    6.0958111751187345e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   4.9037851087980913e-09    4.9037851087980910e-01\n",
      "Overall NLP error.......:   6.0958111751187349e-04    6.0958111751187345e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 688\n",
      "Number of objective gradient evaluations             = 353\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 689\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 376\n",
      "Number of Lagrangian Hessian evaluations             = 374\n",
      "Total seconds in IPOPT                               = 0.399\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.4234802722930908\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "11 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 296\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.0990701630670116e-09    3.0990701630670114e-01\n",
      "Dual infeasibility......:   9.6655335671587559e-07    9.6655335671587565e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5523752512431530e-13    1.5523752512431529e-05\n",
      "Overall NLP error.......:   9.6655335671587559e-07    9.6655335671587565e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 619\n",
      "Number of objective gradient evaluations             = 288\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 619\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 299\n",
      "Number of Lagrangian Hessian evaluations             = 297\n",
      "Total seconds in IPOPT                               = 0.328\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.351886510848999\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "12 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 280\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.5986557886632577e-06    5.5986557886632579e+02\n",
      "Dual infeasibility......:   5.1408356541331017e-04    5.1408356541331013e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.6859629790478345e-09    3.6859629790478343e-01\n",
      "Overall NLP error.......:   5.1408356541331017e-04    5.1408356541331013e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 563\n",
      "Number of objective gradient evaluations             = 270\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 564\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 283\n",
      "Number of Lagrangian Hessian evaluations             = 281\n",
      "Total seconds in IPOPT                               = 0.311\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.3351585865020752\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "13 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 328\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.6243384385804145e-09    3.6243384385804145e-01\n",
      "Dual infeasibility......:   6.4547147008762520e-07    6.4547147008762522e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   9.9999980657239362e-09    9.9999980657239362e-09\n",
      "Complementarity.........:   1.3003113172491350e-12    1.3003113172491349e-04\n",
      "Overall NLP error.......:   6.4547147008762520e-07    6.4547147008762522e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 531\n",
      "Number of objective gradient evaluations             = 330\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 531\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 330\n",
      "Number of Lagrangian Hessian evaluations             = 329\n",
      "Total seconds in IPOPT                               = 0.346\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.3698244094848633\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "14 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 371\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.6393729766584647e-09    2.6393729766584645e-01\n",
      "Dual infeasibility......:   6.4593038201356525e-09    6.4593038201356523e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544337250308721e-13    1.2544337250308721e-05\n",
      "Overall NLP error.......:   6.4593038201356525e-09    6.4593038201356523e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 548\n",
      "Number of objective gradient evaluations             = 372\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 549\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 372\n",
      "Number of Lagrangian Hessian evaluations             = 371\n",
      "Total seconds in IPOPT                               = 0.395\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.41905856132507324\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "15 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 371\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.8435383422065017e-09    1.8435383422065016e-01\n",
      "Dual infeasibility......:   8.3026006872551536e-09    8.3026006872551539e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544302826334679e-13    1.2544302826334679e-05\n",
      "Overall NLP error.......:   8.3026006872551536e-09    8.3026006872551539e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 573\n",
      "Number of objective gradient evaluations             = 372\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 573\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 373\n",
      "Number of Lagrangian Hessian evaluations             = 371\n",
      "Total seconds in IPOPT                               = 0.395\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4187967777252197\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "16 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 290\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.2027743979163269e-09    3.2027743979163270e-01\n",
      "Dual infeasibility......:   7.2784886514381260e-07    7.2784886514381256e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   8.8292724901202519e-12    8.8292724901202512e-04\n",
      "Overall NLP error.......:   7.2784886514381260e-07    7.2784886514381256e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 629\n",
      "Number of objective gradient evaluations             = 292\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 630\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 293\n",
      "Number of Lagrangian Hessian evaluations             = 291\n",
      "Total seconds in IPOPT                               = 0.317\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.34097862243652344\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "17 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 459\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.9941630648654975e-09    4.9941630648654972e-01\n",
      "Dual infeasibility......:   1.3557934265401388e-07    1.3557934265401387e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544302826334684e-13    1.2544302826334684e-05\n",
      "Overall NLP error.......:   1.3557934265401388e-07    1.3557934265401387e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 2150\n",
      "Number of objective gradient evaluations             = 461\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 2150\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 461\n",
      "Number of Lagrangian Hessian evaluations             = 460\n",
      "Total seconds in IPOPT                               = 0.503\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.5276548862457275\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "18 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 401\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5138804573124354e-09    3.5138804573124355e-01\n",
      "Dual infeasibility......:   8.3160532383734945e-09    8.3160532383734942e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2596101008840648e-13    1.2596101008840648e-05\n",
      "Overall NLP error.......:   8.3160532383734945e-09    8.3160532383734942e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 965\n",
      "Number of objective gradient evaluations             = 402\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 965\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 402\n",
      "Number of Lagrangian Hessian evaluations             = 401\n",
      "Total seconds in IPOPT                               = 0.458\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4817211627960205\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "19 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 390\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.9605255684521686e-09    3.9605255684521684e-01\n",
      "Dual infeasibility......:   2.1206922757114766e-09    2.1206922757114766e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3903305158800546e-13    1.3903305158800546e-05\n",
      "Overall NLP error.......:   2.1206922757114766e-09    2.1206922757114766e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 899\n",
      "Number of objective gradient evaluations             = 391\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 899\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 391\n",
      "Number of Lagrangian Hessian evaluations             = 390\n",
      "Total seconds in IPOPT                               = 0.423\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.44733619689941406\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "20 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 377\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.3960689355614626e-09    3.3960689355614626e-01\n",
      "Dual infeasibility......:   8.9416131239172564e-07    8.9416131239172557e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.6256275801000504e-13    1.6256275801000505e-05\n",
      "Overall NLP error.......:   8.9416131239172564e-07    8.9416131239172557e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 597\n",
      "Number of objective gradient evaluations             = 379\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 597\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 379\n",
      "Number of Lagrangian Hessian evaluations             = 378\n",
      "Total seconds in IPOPT                               = 0.400\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.4251258373260498\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "21 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 921\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5510229574803747e-09    3.5510229574803748e-01\n",
      "Dual infeasibility......:   9.6514435191186956e-09    9.6514435191186954e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2545073145974100e-13    1.2545073145974100e-05\n",
      "Overall NLP error.......:   9.6514435191186956e-09    9.6514435191186954e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 7756\n",
      "Number of objective gradient evaluations             = 907\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 7756\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 923\n",
      "Number of Lagrangian Hessian evaluations             = 921\n",
      "Total seconds in IPOPT                               = 1.065\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 1.0885608196258545\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "22 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 384\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.9374033997927626e-09    3.9374033997927627e-01\n",
      "Dual infeasibility......:   5.7730672500086214e-09    5.7730672500086211e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3008240769365323e-13    1.3008240769365323e-05\n",
      "Overall NLP error.......:   5.7730672500086214e-09    5.7730672500086211e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 659\n",
      "Number of objective gradient evaluations             = 385\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 660\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 385\n",
      "Number of Lagrangian Hessian evaluations             = 384\n",
      "Total seconds in IPOPT                               = 0.420\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.44492673873901367\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "23 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 385\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.0672263790486895e-09    2.0672263790486894e-01\n",
      "Dual infeasibility......:   3.7491773555861207e-09    3.7491773555861208e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.1890712240318613e-13    3.1890712240318611e-05\n",
      "Overall NLP error.......:   3.7491773555861207e-09    3.7491773555861208e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 635\n",
      "Number of objective gradient evaluations             = 386\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 635\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 386\n",
      "Number of Lagrangian Hessian evaluations             = 385\n",
      "Total seconds in IPOPT                               = 0.412\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4375729560852051\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "24 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 361\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.9419721215204026e-09    1.9419721215204025e-01\n",
      "Dual infeasibility......:   3.7929521925070730e-09    3.7929521925070731e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2546966081174906e-13    1.2546966081174905e-05\n",
      "Overall NLP error.......:   3.7929521925070730e-09    3.7929521925070731e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 549\n",
      "Number of objective gradient evaluations             = 362\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 550\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 362\n",
      "Number of Lagrangian Hessian evaluations             = 361\n",
      "Total seconds in IPOPT                               = 0.391\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4166140556335449\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "25 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 303\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.1899150152135238e-09    3.1899150152135236e-01\n",
      "Dual infeasibility......:   9.1414124910455082e-09    9.1414124910455086e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.1739052635366779e-13    2.1739052635366778e-05\n",
      "Overall NLP error.......:   9.1414124910455082e-09    9.1414124910455086e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 639\n",
      "Number of objective gradient evaluations             = 303\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 640\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 305\n",
      "Number of Lagrangian Hessian evaluations             = 303\n",
      "Total seconds in IPOPT                               = 0.330\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3536694049835205\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "26 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 370\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.8935518066776353e-06    5.8935518066776353e+02\n",
      "Dual infeasibility......:   5.1501041334090219e-04    5.1501041334090216e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.8585003872951646e-09    3.8585003872951645e-01\n",
      "Overall NLP error.......:   5.1501041334090219e-04    5.1501041334090216e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 905\n",
      "Number of objective gradient evaluations             = 365\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 906\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 373\n",
      "Number of Lagrangian Hessian evaluations             = 371\n",
      "Total seconds in IPOPT                               = 0.410\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.43409228324890137\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "27 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 292\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5121322725464701e-09    3.5121322725464699e-01\n",
      "Dual infeasibility......:   2.2702395169388500e-09    2.2702395169388501e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3051904024718246e-13    1.3051904024718246e-05\n",
      "Overall NLP error.......:   2.2702395169388500e-09    2.2702395169388501e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 445\n",
      "Number of objective gradient evaluations             = 293\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 445\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 293\n",
      "Number of Lagrangian Hessian evaluations             = 292\n",
      "Total seconds in IPOPT                               = 0.320\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3434138298034668\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "28 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 1215\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.7010512384573350e-06    5.7010512384573349e+02\n",
      "Dual infeasibility......:   5.3062655013344138e-04    5.3062655013344134e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.7342602890661164e-08    2.7342602890661163e+00\n",
      "Overall NLP error.......:   5.3062655013344138e-04    5.3062655013344134e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 10858\n",
      "Number of objective gradient evaluations             = 1208\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 10859\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 1218\n",
      "Number of Lagrangian Hessian evaluations             = 1216\n",
      "Total seconds in IPOPT                               = 1.318\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 1.3416152000427246\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "29 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 382\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.0497395620761509e-09    3.0497395620761508e-01\n",
      "Dual infeasibility......:   2.3931123077381536e-09    2.3931123077381536e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.6385812933921625e-13    1.6385812933921625e-05\n",
      "Overall NLP error.......:   2.3931123077381536e-09    2.3931123077381536e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 697\n",
      "Number of objective gradient evaluations             = 383\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 697\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 383\n",
      "Number of Lagrangian Hessian evaluations             = 382\n",
      "Total seconds in IPOPT                               = 0.424\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4490387439727783\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "30 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 313\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7877529107514295e-06    2.7877529107514295e+02\n",
      "Dual infeasibility......:   3.3454815336870348e-04    3.3454815336870350e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   5.5036502385521464e-09    5.5036502385521469e-01\n",
      "Overall NLP error.......:   3.3454815336870348e-04    3.3454815336870350e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 483\n",
      "Number of objective gradient evaluations             = 302\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 484\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 316\n",
      "Number of Lagrangian Hessian evaluations             = 314\n",
      "Total seconds in IPOPT                               = 0.339\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.36483144760131836\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "31 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 503\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.4355291051013256e-09    3.4355291051013254e-01\n",
      "Dual infeasibility......:   4.4243315432937583e-09    3.2047494193906378e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2690071885260718e-13    1.2690071885260718e-05\n",
      "Overall NLP error.......:   4.4243315432937583e-09    3.2047494193906378e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 2749\n",
      "Number of objective gradient evaluations             = 504\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 2749\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 504\n",
      "Number of Lagrangian Hessian evaluations             = 503\n",
      "Total seconds in IPOPT                               = 0.568\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5921304225921631\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "32 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 296\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.0195600100594301e-09    3.0195600100594300e-01\n",
      "Dual infeasibility......:   2.1288521727270748e-07    2.1288521727270748e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5950911713999592e-13    1.5950911713999593e-05\n",
      "Overall NLP error.......:   2.1288521727270748e-07    2.1288521727270748e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 507\n",
      "Number of objective gradient evaluations             = 297\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 507\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 297\n",
      "Number of Lagrangian Hessian evaluations             = 296\n",
      "Total seconds in IPOPT                               = 0.320\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.34439659118652344\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "33 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 391\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.9105962591997883e-09    1.9105962591997883e-01\n",
      "Dual infeasibility......:   5.5360296650552333e-09    5.5360296650552332e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544320683158759e-13    1.2544320683158759e-05\n",
      "Overall NLP error.......:   5.5360296650552333e-09    5.5360296650552332e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 616\n",
      "Number of objective gradient evaluations             = 392\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 617\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 392\n",
      "Number of Lagrangian Hessian evaluations             = 391\n",
      "Total seconds in IPOPT                               = 0.418\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4414215087890625\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "34 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 340\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.3242595560362952e-09    3.3242595560362953e-01\n",
      "Dual infeasibility......:   2.0630898842231090e-09    1.7996221908019952e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2838938268862243e-13    1.2838938268862243e-05\n",
      "Overall NLP error.......:   2.0630898842231090e-09    1.7996221908019952e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 733\n",
      "Number of objective gradient evaluations             = 341\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 733\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 341\n",
      "Number of Lagrangian Hessian evaluations             = 340\n",
      "Total seconds in IPOPT                               = 0.373\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3966550827026367\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "35 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 201\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.3733515390779373e-05    1.3733515390779373e+03\n",
      "Dual infeasibility......:   1.7460732658733220e-03    1.7460732658733221e+05\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.9131994012191564e-08    1.9131994012191564e+00\n",
      "Overall NLP error.......:   1.7460732658733220e-03    1.7460732658733221e+05\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 295\n",
      "Number of objective gradient evaluations             = 183\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 295\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 204\n",
      "Number of Lagrangian Hessian evaluations             = 202\n",
      "Total seconds in IPOPT                               = 0.215\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.23835039138793945\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "36 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 387\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.9302841383800178e-09    2.7293578084797998e-01\n",
      "Dual infeasibility......:   1.8617513038065635e-09    1.7340930839905142e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3720260687803244e-13    1.2779477645924377e-05\n",
      "Overall NLP error.......:   1.8617513038065635e-09    1.7340930839905142e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 710\n",
      "Number of objective gradient evaluations             = 378\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 710\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 389\n",
      "Number of Lagrangian Hessian evaluations             = 387\n",
      "Total seconds in IPOPT                               = 0.416\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.43959736824035645\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "37 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 550\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5201450707024333e-09    3.5201450707024334e-01\n",
      "Dual infeasibility......:   2.1127122845863191e-09    2.1127122845863192e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2556172573156121e-13    1.2556172573156121e-05\n",
      "Overall NLP error.......:   2.1127122845863191e-09    2.1127122845863192e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1407\n",
      "Number of objective gradient evaluations             = 546\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1408\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 552\n",
      "Number of Lagrangian Hessian evaluations             = 550\n",
      "Total seconds in IPOPT                               = 0.616\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.6360342502593994\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "38 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 305\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.4602150802361211e-09    3.4602150802361209e-01\n",
      "Dual infeasibility......:   2.6272479737190633e-07    2.6272479737190633e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5693124336028712e-13    1.5693124336028710e-05\n",
      "Overall NLP error.......:   2.6272479737190633e-07    2.6272479737190633e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 538\n",
      "Number of objective gradient evaluations             = 307\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 539\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 308\n",
      "Number of Lagrangian Hessian evaluations             = 306\n",
      "Total seconds in IPOPT                               = 0.329\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.35202646255493164\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "39 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 349\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.8121290472327995e-06    2.8121290472327996e+02\n",
      "Dual infeasibility......:   3.7358271680064544e-04    3.7358271680064543e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5259801007905830e-09    1.5259801007905829e-01\n",
      "Overall NLP error.......:   3.7358271680064544e-04    3.7358271680064543e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 628\n",
      "Number of objective gradient evaluations             = 325\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 628\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 353\n",
      "Number of Lagrangian Hessian evaluations             = 350\n",
      "Total seconds in IPOPT                               = 0.373\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.39913487434387207\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "40 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 291\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   7.5784005239925444e-09    7.5784005239925445e-01\n",
      "Dual infeasibility......:   3.5623804485075565e-09    3.5623804485075566e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   8.0929677525408819e-13    8.0929677525408821e-05\n",
      "Overall NLP error.......:   3.5623804485075565e-09    3.5623804485075566e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 606\n",
      "Number of objective gradient evaluations             = 292\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 607\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 292\n",
      "Number of Lagrangian Hessian evaluations             = 291\n",
      "Total seconds in IPOPT                               = 0.323\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.34604644775390625\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "41 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 300\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.9627712874780683e-09    3.9627712874780679e-01\n",
      "Dual infeasibility......:   2.2413914189586892e-09    2.2413914189586892e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4022798537550022e-13    1.4022798537550021e-05\n",
      "Overall NLP error.......:   2.2413914189586892e-09    2.2413914189586892e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 552\n",
      "Number of objective gradient evaluations             = 301\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 553\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 301\n",
      "Number of Lagrangian Hessian evaluations             = 300\n",
      "Total seconds in IPOPT                               = 0.327\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3507668972015381\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "42 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 384\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.8932782216764958e-09    2.8932782216764957e-01\n",
      "Dual infeasibility......:   1.3162353979970675e-09    9.2903603594978915e-02\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2689129854904841e-13    1.2689129854904841e-05\n",
      "Overall NLP error.......:   1.3162353979970675e-09    9.2903603594978915e-02\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 824\n",
      "Number of objective gradient evaluations             = 385\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 825\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 385\n",
      "Number of Lagrangian Hessian evaluations             = 384\n",
      "Total seconds in IPOPT                               = 0.412\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4367098808288574\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "43 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 320\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.1465357636821983e-06    3.1465357636821983e+02\n",
      "Dual infeasibility......:   3.6330139898326511e-04    3.6330139898326510e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   5.4335870285035925e-09    5.4335870285035925e-01\n",
      "Overall NLP error.......:   3.6330139898326511e-04    3.6330139898326510e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 546\n",
      "Number of objective gradient evaluations             = 305\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 547\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 324\n",
      "Number of Lagrangian Hessian evaluations             = 321\n",
      "Total seconds in IPOPT                               = 0.344\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.3685479164123535\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "44 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 629\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7317333478079377e-09    2.7317333478079375e-01\n",
      "Dual infeasibility......:   2.9900452849984329e-09    2.9900452849984327e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.6910701327907617e-13    1.6910701327907616e-05\n",
      "Overall NLP error.......:   2.9900452849984329e-09    2.9900452849984327e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 3821\n",
      "Number of objective gradient evaluations             = 620\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 3822\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 631\n",
      "Number of Lagrangian Hessian evaluations             = 629\n",
      "Total seconds in IPOPT                               = 0.698\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.7223656177520752\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "45 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 338\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.9814302667030490e-09    2.9814302667030490e-01\n",
      "Dual infeasibility......:   1.7769441913995138e-09    1.7769441913995138e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2546273911166799e-13    1.2546273911166799e-05\n",
      "Overall NLP error.......:   1.7769441913995138e-09    1.7769441913995138e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 740\n",
      "Number of objective gradient evaluations             = 339\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 741\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 339\n",
      "Number of Lagrangian Hessian evaluations             = 338\n",
      "Total seconds in IPOPT                               = 0.376\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3996388912200928\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "46 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 381\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.2010958280963333e-09    2.5844950412833245e-01\n",
      "Dual infeasibility......:   2.0411996497146858e-09    1.2557367384184065e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4777287552899200e-13    9.0909200758202783e-06\n",
      "Overall NLP error.......:   2.0411996497146858e-09    1.2557367384184065e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 796\n",
      "Number of objective gradient evaluations             = 382\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 796\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 382\n",
      "Number of Lagrangian Hessian evaluations             = 381\n",
      "Total seconds in IPOPT                               = 0.418\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.44257235527038574\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "47 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 252\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.6481724184034887e-09    3.6481724184034886e-01\n",
      "Dual infeasibility......:   4.0477420096896340e-09    4.0477420096896338e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.2473754467413006e-13    3.2473754467413002e-05\n",
      "Overall NLP error.......:   4.0477420096896340e-09    4.0477420096896338e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 425\n",
      "Number of objective gradient evaluations             = 253\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 425\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 254\n",
      "Number of Lagrangian Hessian evaluations             = 252\n",
      "Total seconds in IPOPT                               = 0.274\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.29854440689086914\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "48 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 423\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.0242717661353529e-09    5.0242717661353531e-01\n",
      "Dual infeasibility......:   8.0056599139232313e-09    8.0056599139232310e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3067719622182898e-13    1.3067719622182897e-05\n",
      "Overall NLP error.......:   8.0056599139232313e-09    8.0056599139232310e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1053\n",
      "Number of objective gradient evaluations             = 424\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1053\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 424\n",
      "Number of Lagrangian Hessian evaluations             = 423\n",
      "Total seconds in IPOPT                               = 0.468\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.48868608474731445\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "49 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 339\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.0337036034255439e-06    3.1381431446440041e+02\n",
      "Dual infeasibility......:   4.8670134486971918e-04    3.7864420370273714e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   6.0097395084356071e-09    4.6754607412099963e-01\n",
      "Overall NLP error.......:   4.8670134486971918e-04    3.7864420370273714e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 643\n",
      "Number of objective gradient evaluations             = 333\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 643\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 343\n",
      "Number of Lagrangian Hessian evaluations             = 340\n",
      "Total seconds in IPOPT                               = 0.367\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.39157986640930176\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "50 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 347\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5647621729207569e-09    3.5647621729207568e-01\n",
      "Dual infeasibility......:   1.8715498842381903e-09    1.8715498842381903e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2547111933062287e-13    1.2547111933062286e-05\n",
      "Overall NLP error.......:   1.8715498842381903e-09    1.8715498842381903e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 713\n",
      "Number of objective gradient evaluations             = 348\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 714\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 348\n",
      "Number of Lagrangian Hessian evaluations             = 347\n",
      "Total seconds in IPOPT                               = 0.366\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3899052143096924\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "51 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 482\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.9022492346611128e-09    2.9022492346611128e-01\n",
      "Dual infeasibility......:   2.2096081322540456e-09    1.3596044941111710e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5401715492476289e-13    1.5401715492476288e-05\n",
      "Overall NLP error.......:   2.2096081322540456e-09    1.3596044941111710e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1349\n",
      "Number of objective gradient evaluations             = 478\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1350\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 484\n",
      "Number of Lagrangian Hessian evaluations             = 482\n",
      "Total seconds in IPOPT                               = 0.535\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5600457191467285\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "52 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 466\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.2583405074427958e-09    5.2583405074427958e-01\n",
      "Dual infeasibility......:   4.1314045792326297e-09    4.1314045792326298e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.1689909836723670e-13    2.1689909836723670e-05\n",
      "Overall NLP error.......:   4.1314045792326297e-09    4.1314045792326298e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 985\n",
      "Number of objective gradient evaluations             = 467\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 985\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 467\n",
      "Number of Lagrangian Hessian evaluations             = 466\n",
      "Total seconds in IPOPT                               = 0.492\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5175092220306396\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "53 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 330\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.2391526894662656e-09    3.2391526894662653e-01\n",
      "Dual infeasibility......:   2.6850358771213464e-09    2.6850358771213462e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4013774970251562e-13    1.4013774970251562e-05\n",
      "Overall NLP error.......:   2.6850358771213464e-09    2.6850358771213462e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 518\n",
      "Number of objective gradient evaluations             = 323\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 518\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 332\n",
      "Number of Lagrangian Hessian evaluations             = 330\n",
      "Total seconds in IPOPT                               = 0.352\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3800673484802246\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "54 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 300\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.4264260924076091e-09    3.4264260924076090e-01\n",
      "Dual infeasibility......:   8.0175448036918592e-07    8.0175448036918596e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4901472982719052e-13    1.4901472982719051e-05\n",
      "Overall NLP error.......:   8.0175448036918592e-07    8.0175448036918596e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 584\n",
      "Number of objective gradient evaluations             = 302\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 584\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 302\n",
      "Number of Lagrangian Hessian evaluations             = 301\n",
      "Total seconds in IPOPT                               = 0.322\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.3476295471191406\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "55 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 365\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.2331846998958241e-09    3.2331846998958241e-01\n",
      "Dual infeasibility......:   3.4551873862886613e-07    3.4551873862886616e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.9927047977075433e-13    1.9927047977075432e-05\n",
      "Overall NLP error.......:   3.4551873862886613e-07    3.4551873862886616e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 904\n",
      "Number of objective gradient evaluations             = 351\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 905\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 370\n",
      "Number of Lagrangian Hessian evaluations             = 366\n",
      "Total seconds in IPOPT                               = 0.399\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.423520565032959\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "56 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 360\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.0964884620394486e-06    3.0964884620394486e+02\n",
      "Dual infeasibility......:   3.7572540179136931e-04    3.7572540179136930e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   9.5835491294835181e-09    9.5835491294835173e-01\n",
      "Overall NLP error.......:   3.7572540179136931e-04    3.7572540179136930e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 744\n",
      "Number of objective gradient evaluations             = 339\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 744\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 363\n",
      "Number of Lagrangian Hessian evaluations             = 361\n",
      "Total seconds in IPOPT                               = 0.380\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.40377020835876465\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "57 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 397\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   1.6950190633610312e-09    1.6950190633610313e-01\n",
      "Dual infeasibility......:   2.6063762108048001e-09    2.6063762108048000e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2563579840372578e-13    1.2563579840372577e-05\n",
      "Overall NLP error.......:   2.6063762108048001e-09    2.6063762108048000e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 630\n",
      "Number of objective gradient evaluations             = 398\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 631\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 398\n",
      "Number of Lagrangian Hessian evaluations             = 397\n",
      "Total seconds in IPOPT                               = 0.423\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4469280242919922\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "58 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 303\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.9963366752672952e-09    3.9963366752672952e-01\n",
      "Dual infeasibility......:   5.7404947456593534e-09    5.7404947456593536e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2736133295592734e-13    1.2736133295592733e-05\n",
      "Overall NLP error.......:   5.7404947456593534e-09    5.7404947456593536e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 485\n",
      "Number of objective gradient evaluations             = 304\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 486\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 305\n",
      "Number of Lagrangian Hessian evaluations             = 303\n",
      "Total seconds in IPOPT                               = 0.332\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.35585451126098633\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "59 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 338\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.8663019104528797e-09    2.8663019104528797e-01\n",
      "Dual infeasibility......:   3.5075746968259942e-09    2.2114928190336364e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4840769760127882e-13    1.4840769760127882e-05\n",
      "Overall NLP error.......:   3.5075746968259942e-09    2.2114928190336364e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 511\n",
      "Number of objective gradient evaluations             = 339\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 511\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 339\n",
      "Number of Lagrangian Hessian evaluations             = 338\n",
      "Total seconds in IPOPT                               = 0.369\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.39336228370666504\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "60 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 269\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.4800165795741188e-09    3.4800165795741189e-01\n",
      "Dual infeasibility......:   5.4971638302237722e-09    5.4971638302237724e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544303367631448e-13    1.2544303367631448e-05\n",
      "Overall NLP error.......:   5.4971638302237722e-09    5.4971638302237724e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 462\n",
      "Number of objective gradient evaluations             = 270\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 462\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 271\n",
      "Number of Lagrangian Hessian evaluations             = 269\n",
      "Total seconds in IPOPT                               = 0.297\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3211245536804199\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "61 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 300\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5581730932880663e-09    3.5581730932880662e-01\n",
      "Dual infeasibility......:   6.9007152988813662e-07    6.9007152988813658e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.7604284524584434e-13    2.7604284524584434e-05\n",
      "Overall NLP error.......:   6.9007152988813662e-07    6.9007152988813658e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 608\n",
      "Number of objective gradient evaluations             = 302\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 608\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 302\n",
      "Number of Lagrangian Hessian evaluations             = 301\n",
      "Total seconds in IPOPT                               = 0.333\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.3573458194732666\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "62 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 233\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.3400569427379529e-06    4.3400569427379526e+02\n",
      "Dual infeasibility......:   1.1941149761055786e-03    1.1941149761055785e+05\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.3239828097459124e-09    2.3239828097459123e-01\n",
      "Overall NLP error.......:   1.1941149761055786e-03    1.1941149761055785e+05\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 322\n",
      "Number of objective gradient evaluations             = 221\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 323\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 236\n",
      "Number of Lagrangian Hessian evaluations             = 234\n",
      "Total seconds in IPOPT                               = 0.251\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.27698755264282227\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "63 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 311\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.8497960886246459e-09    2.8497960886246460e-01\n",
      "Dual infeasibility......:   1.6721695415846742e-09    1.6721695415846741e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544316645252029e-13    1.2544316645252028e-05\n",
      "Overall NLP error.......:   1.6721695415846742e-09    1.6721695415846741e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 522\n",
      "Number of objective gradient evaluations             = 312\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 522\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 312\n",
      "Number of Lagrangian Hessian evaluations             = 311\n",
      "Total seconds in IPOPT                               = 0.342\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.36633920669555664\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "64 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 485\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.9361480536029799e-09    4.9361480536029800e-01\n",
      "Dual infeasibility......:   5.9016702289407439e-09    5.9016702289407441e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3721855813399666e-13    1.3721855813399666e-05\n",
      "Overall NLP error.......:   5.9016702289407439e-09    5.9016702289407441e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1402\n",
      "Number of objective gradient evaluations             = 469\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1403\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 488\n",
      "Number of Lagrangian Hessian evaluations             = 485\n",
      "Total seconds in IPOPT                               = 0.521\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5411577224731445\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "65 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 384\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.4922626007741724e-09    4.4922626007741723e-01\n",
      "Dual infeasibility......:   6.8192677493344586e-09    6.8192677493344589e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2741667362752374e-13    1.2741667362752375e-05\n",
      "Overall NLP error.......:   6.8192677493344586e-09    6.8192677493344589e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 716\n",
      "Number of objective gradient evaluations             = 385\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 716\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 385\n",
      "Number of Lagrangian Hessian evaluations             = 384\n",
      "Total seconds in IPOPT                               = 0.416\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.44066834449768066\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "66 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 461\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7536502162943843e-09    2.7536502162943843e-01\n",
      "Dual infeasibility......:   4.7941943274133971e-09    4.7941943274133969e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3304391866956622e-13    1.3304391866956622e-05\n",
      "Overall NLP error.......:   4.7941943274133971e-09    4.7941943274133969e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 893\n",
      "Number of objective gradient evaluations             = 452\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 893\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 463\n",
      "Number of Lagrangian Hessian evaluations             = 461\n",
      "Total seconds in IPOPT                               = 0.508\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5321402549743652\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "67 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 528\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7088345899433226e-06    2.7088345899433227e+02\n",
      "Dual infeasibility......:   3.5256682781482831e-04    3.5256682781482828e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.7709851846948056e-09    1.7709851846948055e-01\n",
      "Overall NLP error.......:   3.5256682781482831e-04    3.5256682781482828e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 3569\n",
      "Number of objective gradient evaluations             = 516\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 3569\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 533\n",
      "Number of Lagrangian Hessian evaluations             = 529\n",
      "Total seconds in IPOPT                               = 0.574\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.5996575355529785\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "68 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 430\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7598831231254511e-09    2.7598831231254511e-01\n",
      "Dual infeasibility......:   5.9726442152333802e-07    5.9726442152333803e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.0807492113422458e-13    2.0807492113422457e-05\n",
      "Overall NLP error.......:   5.9726442152333802e-07    5.9726442152333803e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1901\n",
      "Number of objective gradient evaluations             = 431\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1902\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 431\n",
      "Number of Lagrangian Hessian evaluations             = 430\n",
      "Total seconds in IPOPT                               = 0.479\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.5041625499725342\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "69 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 394\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.8332163351880736e-09    3.8332163351880733e-01\n",
      "Dual infeasibility......:   9.6248021877425878e-09    9.6248021877425882e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5133070662637011e-13    1.5133070662637011e-05\n",
      "Overall NLP error.......:   9.6248021877425878e-09    9.6248021877425882e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 692\n",
      "Number of objective gradient evaluations             = 395\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 692\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 395\n",
      "Number of Lagrangian Hessian evaluations             = 394\n",
      "Total seconds in IPOPT                               = 0.429\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4540586471557617\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "70 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 357\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.2274319391760134e-09    3.2274319391760131e-01\n",
      "Dual infeasibility......:   7.3888466258108987e-09    7.3888466258108987e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2560522119024490e-13    1.2560522119024490e-05\n",
      "Overall NLP error.......:   7.3888466258108987e-09    7.3888466258108987e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 734\n",
      "Number of objective gradient evaluations             = 353\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 734\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 359\n",
      "Number of Lagrangian Hessian evaluations             = 357\n",
      "Total seconds in IPOPT                               = 0.386\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.41029834747314453\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "71 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 451\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.7276052151484919e-09    2.7276052151484920e-01\n",
      "Dual infeasibility......:   1.8220489689175617e-09    1.8220489689175617e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2559011636801384e-13    1.2559011636801384e-05\n",
      "Overall NLP error.......:   1.8220489689175617e-09    1.8220489689175617e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 769\n",
      "Number of objective gradient evaluations             = 452\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 769\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 452\n",
      "Number of Lagrangian Hessian evaluations             = 451\n",
      "Total seconds in IPOPT                               = 0.492\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.516157865524292\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "72 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 299\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.6776975428786317e-09    2.6776975428786315e-01\n",
      "Dual infeasibility......:   1.0109170495171341e-09    7.1781786961030103e-02\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544334281346515e-13    1.2544334281346514e-05\n",
      "Overall NLP error.......:   1.0109170495171341e-09    7.1781786961030103e-02\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 513\n",
      "Number of objective gradient evaluations             = 300\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 514\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 301\n",
      "Number of Lagrangian Hessian evaluations             = 299\n",
      "Total seconds in IPOPT                               = 0.328\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3552820682525635\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "73 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 238\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.4713017654980192e-06    4.4713017654980189e+02\n",
      "Dual infeasibility......:   4.9785089857070372e-04    4.9785089857070372e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.6483055571521965e-08    1.6483055571521965e+00\n",
      "Overall NLP error.......:   4.9785089857070372e-04    4.9785089857070372e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 342\n",
      "Number of objective gradient evaluations             = 222\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 342\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 241\n",
      "Number of Lagrangian Hessian evaluations             = 239\n",
      "Total seconds in IPOPT                               = 0.257\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.28243041038513184\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "74 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 508\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.0328260619192422e-09    3.0328260619192421e-01\n",
      "Dual infeasibility......:   2.4734339208439520e-09    2.4734339208439521e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2633230215011090e-13    1.2633230215011089e-05\n",
      "Overall NLP error.......:   2.4734339208439520e-09    2.4734339208439521e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1672\n",
      "Number of objective gradient evaluations             = 509\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1673\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 511\n",
      "Number of Lagrangian Hessian evaluations             = 508\n",
      "Total seconds in IPOPT                               = 0.545\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.5745787620544434\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "75 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 330\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   7.4445718171043553e-09    7.4445718171043551e-01\n",
      "Dual infeasibility......:   3.4567062297352829e-09    3.4567062297352830e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   9.4050520702956447e-13    9.4050520702956443e-05\n",
      "Overall NLP error.......:   3.4567062297352829e-09    3.4567062297352830e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 494\n",
      "Number of objective gradient evaluations             = 331\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 494\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 331\n",
      "Number of Lagrangian Hessian evaluations             = 330\n",
      "Total seconds in IPOPT                               = 0.356\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3817586898803711\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "76 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 361\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   6.1432097173178977e-06    6.1432097173178977e+02\n",
      "Dual infeasibility......:   5.6441578390796553e-04    5.6441578390796552e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.8686525457507863e-09    3.8686525457507864e-01\n",
      "Overall NLP error.......:   5.6441578390796553e-04    5.6441578390796552e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 736\n",
      "Number of objective gradient evaluations             = 351\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 736\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 364\n",
      "Number of Lagrangian Hessian evaluations             = 362\n",
      "Total seconds in IPOPT                               = 0.384\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.40990328788757324\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "77 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 292\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.1694870345774291e-09    2.9889037891302550e-01\n",
      "Dual infeasibility......:   9.7061970842289407e-09    9.1531812329892615e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4638911238036947e-13    1.3804851318454322e-05\n",
      "Overall NLP error.......:   9.7061970842289407e-09    9.1531812329892615e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 540\n",
      "Number of objective gradient evaluations             = 293\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 540\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 293\n",
      "Number of Lagrangian Hessian evaluations             = 292\n",
      "Total seconds in IPOPT                               = 0.320\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.34661054611206055\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "78 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 247\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.8019057370361996e-06    3.6219803671962984e+02\n",
      "Dual infeasibility......:   4.1249308231425523e-04    3.9297182757382332e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   4.0686338242051822e-09    3.8760855349532969e-01\n",
      "Overall NLP error.......:   4.1249308231425523e-04    3.9297182757382332e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 412\n",
      "Number of objective gradient evaluations             = 242\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 412\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 250\n",
      "Number of Lagrangian Hessian evaluations             = 248\n",
      "Total seconds in IPOPT                               = 0.269\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.29744815826416016\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "79 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 374\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5491572112076282e-09    3.5491572112076281e-01\n",
      "Dual infeasibility......:   3.4641657293594581e-09    3.4641657293594580e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.3962460731008827e-13    1.3962460731008827e-05\n",
      "Overall NLP error.......:   3.4641657293594581e-09    3.4641657293594580e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 946\n",
      "Number of objective gradient evaluations             = 375\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 947\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 376\n",
      "Number of Lagrangian Hessian evaluations             = 374\n",
      "Total seconds in IPOPT                               = 0.414\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4398820400238037\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "80 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 310\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.2339236404057469e-09    4.2339236404057468e-01\n",
      "Dual infeasibility......:   1.1526693774895933e-09    1.1526693774895934e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4433722174250404e-13    1.4433722174250404e-05\n",
      "Overall NLP error.......:   1.1526693774895933e-09    1.1526693774895934e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 461\n",
      "Number of objective gradient evaluations             = 311\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 462\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 311\n",
      "Number of Lagrangian Hessian evaluations             = 310\n",
      "Total seconds in IPOPT                               = 0.325\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.34898877143859863\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "81 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 340\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.0899998690373695e-09    2.0899998690373695e-01\n",
      "Dual infeasibility......:   9.8170777546381625e-09    9.8170777546381627e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544455621972793e-13    1.2544455621972793e-05\n",
      "Overall NLP error.......:   9.8170777546381625e-09    9.8170777546381627e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 629\n",
      "Number of objective gradient evaluations             = 341\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 630\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 341\n",
      "Number of Lagrangian Hessian evaluations             = 340\n",
      "Total seconds in IPOPT                               = 0.368\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3869946002960205\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "82 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 435\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   6.9810586680405877e-06    6.9810586680405879e+02\n",
      "Dual infeasibility......:   6.2029061456045571e-04    6.2029061456045572e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   4.5517018580853721e-09    4.5517018580853719e-01\n",
      "Overall NLP error.......:   6.2029061456045571e-04    6.2029061456045572e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1697\n",
      "Number of objective gradient evaluations             = 425\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1698\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 438\n",
      "Number of Lagrangian Hessian evaluations             = 436\n",
      "Total seconds in IPOPT                               = 0.468\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.4929769039154053\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "83 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 377\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.3434168855276214e-09    3.3434168855276214e-01\n",
      "Dual infeasibility......:   4.6473409503418528e-09    1.9622465402400688e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.1439447098276044e-13    2.1439447098276044e-05\n",
      "Overall NLP error.......:   4.6473409503418528e-09    1.9622465402400688e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 715\n",
      "Number of objective gradient evaluations             = 378\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 716\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 378\n",
      "Number of Lagrangian Hessian evaluations             = 377\n",
      "Total seconds in IPOPT                               = 0.419\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.44387149810791016\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "84 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 297\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.1154159200784901e-06    4.1154159200784898e+02\n",
      "Dual infeasibility......:   4.2175515875507750e-04    4.2175515875507750e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.6897153353608675e-08    1.6897153353608676e+00\n",
      "Overall NLP error.......:   4.2175515875507750e-04    4.2175515875507750e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 478\n",
      "Number of objective gradient evaluations             = 286\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 478\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 300\n",
      "Number of Lagrangian Hessian evaluations             = 298\n",
      "Total seconds in IPOPT                               = 0.325\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.34889936447143555\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "85 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 324\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   4.9231273728528121e-09    4.9231273728528119e-01\n",
      "Dual infeasibility......:   4.7601107914442284e-09    4.6432796964343281e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.9518737399101712e-13    3.9518737399101711e-05\n",
      "Overall NLP error.......:   4.7601107914442284e-09    4.6432796964343281e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 592\n",
      "Number of objective gradient evaluations             = 325\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 593\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 325\n",
      "Number of Lagrangian Hessian evaluations             = 324\n",
      "Total seconds in IPOPT                               = 0.348\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3722515106201172\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "86 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 383\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5940960519807251e-09    3.5940960519807252e-01\n",
      "Dual infeasibility......:   3.2846303635423682e-07    3.2846303635423681e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.5059208146669621e-13    1.5059208146669621e-05\n",
      "Overall NLP error.......:   3.2846303635423682e-07    3.2846303635423681e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 758\n",
      "Number of objective gradient evaluations             = 385\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 759\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 385\n",
      "Number of Lagrangian Hessian evaluations             = 384\n",
      "Total seconds in IPOPT                               = 0.427\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.45122289657592773\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "87 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 329\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.0516753855033660e-09    2.0516753855033659e-01\n",
      "Dual infeasibility......:   3.1698522090277467e-09    3.1698522090277464e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2579315041603952e-13    1.2579315041603952e-05\n",
      "Overall NLP error.......:   3.1698522090277467e-09    3.1698522090277464e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 751\n",
      "Number of objective gradient evaluations             = 330\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 751\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 330\n",
      "Number of Lagrangian Hessian evaluations             = 329\n",
      "Total seconds in IPOPT                               = 0.364\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.3885188102722168\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "88 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 314\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.0840456816227127e-09    2.0840456816227126e-01\n",
      "Dual infeasibility......:   3.4824537213797519e-09    3.4824537213797518e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544303203870740e-13    1.2544303203870740e-05\n",
      "Overall NLP error.......:   3.4824537213797519e-09    3.4824537213797518e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 567\n",
      "Number of objective gradient evaluations             = 315\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 568\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 315\n",
      "Number of Lagrangian Hessian evaluations             = 314\n",
      "Total seconds in IPOPT                               = 0.340\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.35916805267333984\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "89 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 395\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   2.8053390358209256e-09    2.8053390358209257e-01\n",
      "Dual infeasibility......:   3.1067371489493370e-09    1.3243392940288368e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.6438976978859477e-13    1.6438976978859477e-05\n",
      "Overall NLP error.......:   3.1067371489493370e-09    1.3243392940288368e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 601\n",
      "Number of objective gradient evaluations             = 396\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 601\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 396\n",
      "Number of Lagrangian Hessian evaluations             = 395\n",
      "Total seconds in IPOPT                               = 0.426\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4500393867492676\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "90 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 300\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   8.0484806354133040e-09    8.0484806354133043e-01\n",
      "Dual infeasibility......:   9.0826407227050480e-09    9.0826407227050476e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   9.4283108862623443e-13    9.4283108862623443e-05\n",
      "Overall NLP error.......:   9.0826407227050480e-09    9.0826407227050476e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 565\n",
      "Number of objective gradient evaluations             = 292\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 565\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 302\n",
      "Number of Lagrangian Hessian evaluations             = 300\n",
      "Total seconds in IPOPT                               = 0.328\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.356703519821167\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "91 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 470\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.3107048782911991e-09    3.3107048782911991e-01\n",
      "Dual infeasibility......:   6.4669988727605947e-07    6.4669988727605954e+01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.8098671806975983e-13    1.8098671806975984e-05\n",
      "Overall NLP error.......:   6.4669988727605947e-07    6.4669988727605954e+01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1065\n",
      "Number of objective gradient evaluations             = 472\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1065\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 472\n",
      "Number of Lagrangian Hessian evaluations             = 471\n",
      "Total seconds in IPOPT                               = 0.510\n",
      "\n",
      "EXIT: Solved To Acceptable Level.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Solved To Acceptable Level.\n",
      "  Termination condition: optimal\n",
      "  Id: 1\n",
      "  Error rc: 0\n",
      "  Time: 0.5344500541687012\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "92 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 313\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   5.4509024931684988e-06    5.4509024931684985e+02\n",
      "Dual infeasibility......:   5.3288829076146192e-04    5.3288829076146190e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   3.3039529623111930e-09    3.3039529623111930e-01\n",
      "Overall NLP error.......:   5.3288829076146192e-04    5.3288829076146190e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 790\n",
      "Number of objective gradient evaluations             = 308\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 790\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 316\n",
      "Number of Lagrangian Hessian evaluations             = 314\n",
      "Total seconds in IPOPT                               = 0.335\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.3589444160461426\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "93 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 351\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.5711720686050729e-09    3.5711720686050730e-01\n",
      "Dual infeasibility......:   3.2690922833975850e-09    3.2690922833975850e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2546537559728400e-13    1.2546537559728400e-05\n",
      "Overall NLP error.......:   3.2690922833975850e-09    3.2690922833975850e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 670\n",
      "Number of objective gradient evaluations             = 345\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 670\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 353\n",
      "Number of Lagrangian Hessian evaluations             = 351\n",
      "Total seconds in IPOPT                               = 0.381\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.40584707260131836\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "94 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 338\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.1474726582253762e-09    3.1474726582253759e-01\n",
      "Dual infeasibility......:   1.9997475019956302e-09    1.9997475019956301e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.4396733191027071e-13    1.4396733191027071e-05\n",
      "Overall NLP error.......:   1.9997475019956302e-09    1.9997475019956301e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 817\n",
      "Number of objective gradient evaluations             = 339\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 817\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 339\n",
      "Number of Lagrangian Hessian evaluations             = 338\n",
      "Total seconds in IPOPT                               = 0.367\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.39171695709228516\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "95 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 302\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.2832065208704154e-09    3.2832065208704153e-01\n",
      "Dual infeasibility......:   7.2397779350788686e-10    7.2397779350788685e-02\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2628663709803933e-13    1.2628663709803934e-05\n",
      "Overall NLP error.......:   7.2397779350788686e-10    7.2397779350788685e-02\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 530\n",
      "Number of objective gradient evaluations             = 303\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 531\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 303\n",
      "Number of Lagrangian Hessian evaluations             = 302\n",
      "Total seconds in IPOPT                               = 0.331\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.35486936569213867\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "96 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 292\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.1842539436206512e-09    3.1842539436206513e-01\n",
      "Dual infeasibility......:   5.1480781543306637e-09    5.1480781543306642e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2544314439843232e-13    1.2544314439843231e-05\n",
      "Overall NLP error.......:   5.1480781543306637e-09    5.1480781543306642e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 559\n",
      "Number of objective gradient evaluations             = 284\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 560\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 294\n",
      "Number of Lagrangian Hessian evaluations             = 292\n",
      "Total seconds in IPOPT                               = 0.325\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.34821534156799316\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "97 ok optimal\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 381\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.8817930645203892e-06    3.8817930645203893e+02\n",
      "Dual infeasibility......:   4.6011546632181117e-04    4.6011546632181118e+04\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   2.1531274316507906e-09    2.1531274316507906e-01\n",
      "Overall NLP error.......:   4.6011546632181117e-04    4.6011546632181118e+04\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 1052\n",
      "Number of objective gradient evaluations             = 367\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 1053\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 385\n",
      "Number of Lagrangian Hessian evaluations             = 382\n",
      "Total seconds in IPOPT                               = 0.442\n",
      "\n",
      "EXIT: Converged to a point of local infeasibility. Problem may be infeasible.\n",
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "model.name=\"iGFA\";\n",
      "    - termination condition: infeasible\n",
      "    - message from solver: Ipopt 3.14.16\\x3a Converged to a locally infeasible\n",
      "      point. Problem may be infeasible.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: warning\n",
      "  Message: Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.\n",
      "  Termination condition: infeasible\n",
      "  Id: 200\n",
      "  Error rc: 0\n",
      "  Time: 0.4676368236541748\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "98 warning infeasible\n",
      "------------------------------------------------------------------------------\n",
      "Ipopt 3.14.16: max_iter=100000\n",
      "linear_solver=mumps\n",
      "warm_start_init_point=yes\n",
      "print_level=4\n",
      "\n",
      "\n",
      "******************************************************************************\n",
      "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
      " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
      "         For more information visit https://github.com/coin-or/Ipopt\n",
      "******************************************************************************\n",
      "\n",
      "Total number of variables............................:       70\n",
      "                     variables with only lower bounds:       12\n",
      "                variables with lower and upper bounds:       58\n",
      "                     variables with only upper bounds:        0\n",
      "Total number of equality constraints.................:        0\n",
      "Total number of inequality constraints...............:       53\n",
      "        inequality constraints with only lower bounds:       52\n",
      "   inequality constraints with lower and upper bounds:        0\n",
      "        inequality constraints with only upper bounds:        1\n",
      "\n",
      "\n",
      "Number of Iterations....: 364\n",
      "\n",
      "                                   (scaled)                 (unscaled)\n",
      "Objective...............:   3.1296560275211738e-09    3.1296560275211738e-01\n",
      "Dual infeasibility......:   7.1871330280299137e-09    7.1871330280299139e-01\n",
      "Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00\n",
      "Complementarity.........:   1.2567224828658512e-13    1.2567224828658512e-05\n",
      "Overall NLP error.......:   7.1871330280299137e-09    7.1871330280299139e-01\n",
      "\n",
      "\n",
      "Number of objective function evaluations             = 782\n",
      "Number of objective gradient evaluations             = 365\n",
      "Number of equality constraint evaluations            = 0\n",
      "Number of inequality constraint evaluations          = 782\n",
      "Number of equality constraint Jacobian evaluations   = 0\n",
      "Number of inequality constraint Jacobian evaluations = 365\n",
      "Number of Lagrangian Hessian evaluations             = 364\n",
      "Total seconds in IPOPT                               = 0.399\n",
      "\n",
      "EXIT: Optimal Solution Found.\n",
      "\n",
      "Problem: \n",
      "- Lower bound: -inf\n",
      "  Upper bound: inf\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 53\n",
      "  Number of variables: 84\n",
      "  Sense: unknown\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4233970642089844\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n",
      "\n",
      "99 ok optimal\n"
     ]
    }
   ],
   "source": [
    "results_dict, status_run = init_model.run_multistart(instance, strategy='rand_guess_and_bound', iterations=100, suppress_warning=True, \n",
    "                                                     solver_options={'print_level': 4})  # 'tol': 1e-5, 'constr_viol_tol': 1e-5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f3293a1-34a3-484c-b918-9e6679a4a467",
   "metadata": {},
   "source": [
    "## Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5eaa6097-7dbe-4c09-83ce-2cdc7607fc17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle_out = open(os.path.join(mainDir, 'results', 'Lee et al', pH_scale+'_fitlux_fitbeta.pickle'), 'wb')\n",
    "# pickle.dump(results_dict, pickle_out)\n",
    "# pickle_out.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c678d56-ca7c-46d6-95de-ca5df7c8853c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aaa42984-f1f4-4ebc-9534-94a625b4c8e3",
   "metadata": {
    "editable": true,
    "scrolled": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
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       "#T_7247e_row1_col1, #T_7247e_row1_col2, #T_7247e_row1_col4, #T_7247e_row2_col1, #T_7247e_row2_col2, #T_7247e_row2_col4, #T_7247e_row3_col2, #T_7247e_row3_col4, #T_7247e_row4_col2, #T_7247e_row4_col4, #T_7247e_row10_col3, #T_7247e_row44_col3, #T_7247e_row52_col1, #T_7247e_row53_col3, #T_7247e_row57_col3, #T_7247e_row58_col1, #T_7247e_row65_col5, #T_7247e_row88_col5, #T_7247e_row91_col5, #T_7247e_row93_col5 {\n",
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       "</style>\n",
       "<table id=\"T_7247e\">\n",
       "  <caption><b>Objective in 100 multistart runs sorted by objective (showing top 100)</b></caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_7247e_level0_col0\" class=\"col_heading level0 col0\" >Runs</th>\n",
       "      <th id=\"T_7247e_level0_col1\" class=\"col_heading level0 col1\" >fit_betas</th>\n",
       "      <th id=\"T_7247e_level0_col2\" class=\"col_heading level0 col2\" >fit_flux</th>\n",
       "      <th id=\"T_7247e_level0_col3\" class=\"col_heading level0 col3\" >fit_spec_prod</th>\n",
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       "      <th id=\"T_7247e_level0_col6\" class=\"col_heading level0 col6\" >message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row0_col0\" class=\"data row0 col0\" >58</td>\n",
       "      <td id=\"T_7247e_row0_col1\" class=\"data row0 col1\" >0.038964</td>\n",
       "      <td id=\"T_7247e_row0_col2\" class=\"data row0 col2\" >0.106549</td>\n",
       "      <td id=\"T_7247e_row0_col3\" class=\"data row0 col3\" >0.023989</td>\n",
       "      <td id=\"T_7247e_row0_col4\" class=\"data row0 col4\" >0.169502</td>\n",
       "      <td id=\"T_7247e_row0_col5\" class=\"data row0 col5\" >68.599830</td>\n",
       "      <td id=\"T_7247e_row0_col6\" class=\"data row0 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row1_col0\" class=\"data row1 col0\" >16</td>\n",
       "      <td id=\"T_7247e_row1_col1\" class=\"data row1 col1\" >0.042025</td>\n",
       "      <td id=\"T_7247e_row1_col2\" class=\"data row1 col2\" >0.117618</td>\n",
       "      <td id=\"T_7247e_row1_col3\" class=\"data row1 col3\" >0.024711</td>\n",
       "      <td id=\"T_7247e_row1_col4\" class=\"data row1 col4\" >0.184354</td>\n",
       "      <td id=\"T_7247e_row1_col5\" class=\"data row1 col5\" >68.713055</td>\n",
       "      <td id=\"T_7247e_row1_col6\" class=\"data row1 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row2_col0\" class=\"data row2 col0\" >34</td>\n",
       "      <td id=\"T_7247e_row2_col1\" class=\"data row2 col1\" >0.040866</td>\n",
       "      <td id=\"T_7247e_row2_col2\" class=\"data row2 col2\" >0.125645</td>\n",
       "      <td id=\"T_7247e_row2_col3\" class=\"data row2 col3\" >0.024549</td>\n",
       "      <td id=\"T_7247e_row2_col4\" class=\"data row2 col4\" >0.191060</td>\n",
       "      <td id=\"T_7247e_row2_col5\" class=\"data row2 col5\" >68.529072</td>\n",
       "      <td id=\"T_7247e_row2_col6\" class=\"data row2 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row3_col0\" class=\"data row3 col0\" >25</td>\n",
       "      <td id=\"T_7247e_row3_col1\" class=\"data row3 col1\" >0.046068</td>\n",
       "      <td id=\"T_7247e_row3_col2\" class=\"data row3 col2\" >0.122862</td>\n",
       "      <td id=\"T_7247e_row3_col3\" class=\"data row3 col3\" >0.025267</td>\n",
       "      <td id=\"T_7247e_row3_col4\" class=\"data row3 col4\" >0.194197</td>\n",
       "      <td id=\"T_7247e_row3_col5\" class=\"data row3 col5\" >84.724603</td>\n",
       "      <td id=\"T_7247e_row3_col6\" class=\"data row3 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row4_col0\" class=\"data row4 col0\" >88</td>\n",
       "      <td id=\"T_7247e_row4_col1\" class=\"data row4 col1\" >0.047540</td>\n",
       "      <td id=\"T_7247e_row4_col2\" class=\"data row4 col2\" >0.132617</td>\n",
       "      <td id=\"T_7247e_row4_col3\" class=\"data row4 col3\" >0.025011</td>\n",
       "      <td id=\"T_7247e_row4_col4\" class=\"data row4 col4\" >0.205168</td>\n",
       "      <td id=\"T_7247e_row4_col5\" class=\"data row4 col5\" >69.192132</td>\n",
       "      <td id=\"T_7247e_row4_col6\" class=\"data row4 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row5_col0\" class=\"data row5 col0\" >24</td>\n",
       "      <td id=\"T_7247e_row5_col1\" class=\"data row5 col1\" >0.044848</td>\n",
       "      <td id=\"T_7247e_row5_col2\" class=\"data row5 col2\" >0.136617</td>\n",
       "      <td id=\"T_7247e_row5_col3\" class=\"data row5 col3\" >0.025258</td>\n",
       "      <td id=\"T_7247e_row5_col4\" class=\"data row5 col4\" >0.206723</td>\n",
       "      <td id=\"T_7247e_row5_col5\" class=\"data row5 col5\" >68.765661</td>\n",
       "      <td id=\"T_7247e_row5_col6\" class=\"data row5 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row6_col0\" class=\"data row6 col0\" >89</td>\n",
       "      <td id=\"T_7247e_row6_col1\" class=\"data row6 col1\" >0.047693</td>\n",
       "      <td id=\"T_7247e_row6_col2\" class=\"data row6 col2\" >0.136761</td>\n",
       "      <td id=\"T_7247e_row6_col3\" class=\"data row6 col3\" >0.023950</td>\n",
       "      <td id=\"T_7247e_row6_col4\" class=\"data row6 col4\" >0.208405</td>\n",
       "      <td id=\"T_7247e_row6_col5\" class=\"data row6 col5\" >68.775684</td>\n",
       "      <td id=\"T_7247e_row6_col6\" class=\"data row6 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row7_col0\" class=\"data row7 col0\" >82</td>\n",
       "      <td id=\"T_7247e_row7_col1\" class=\"data row7 col1\" >0.049582</td>\n",
       "      <td id=\"T_7247e_row7_col2\" class=\"data row7 col2\" >0.134569</td>\n",
       "      <td id=\"T_7247e_row7_col3\" class=\"data row7 col3\" >0.024849</td>\n",
       "      <td id=\"T_7247e_row7_col4\" class=\"data row7 col4\" >0.209000</td>\n",
       "      <td id=\"T_7247e_row7_col5\" class=\"data row7 col5\" >68.562238</td>\n",
       "      <td id=\"T_7247e_row7_col6\" class=\"data row7 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row8_col0\" class=\"data row8 col0\" >5</td>\n",
       "      <td id=\"T_7247e_row8_col1\" class=\"data row8 col1\" >0.047937</td>\n",
       "      <td id=\"T_7247e_row8_col2\" class=\"data row8 col2\" >0.144373</td>\n",
       "      <td id=\"T_7247e_row8_col3\" class=\"data row8 col3\" >0.026489</td>\n",
       "      <td id=\"T_7247e_row8_col4\" class=\"data row8 col4\" >0.218799</td>\n",
       "      <td id=\"T_7247e_row8_col5\" class=\"data row8 col5\" >68.376858</td>\n",
       "      <td id=\"T_7247e_row8_col6\" class=\"data row8 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row9_col0\" class=\"data row9 col0\" >47</td>\n",
       "      <td id=\"T_7247e_row9_col1\" class=\"data row9 col1\" >0.052234</td>\n",
       "      <td id=\"T_7247e_row9_col2\" class=\"data row9 col2\" >0.181607</td>\n",
       "      <td id=\"T_7247e_row9_col3\" class=\"data row9 col3\" >0.024608</td>\n",
       "      <td id=\"T_7247e_row9_col4\" class=\"data row9 col4\" >0.258450</td>\n",
       "      <td id=\"T_7247e_row9_col5\" class=\"data row9 col5\" >68.362714</td>\n",
       "      <td id=\"T_7247e_row9_col6\" class=\"data row9 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row10_col0\" class=\"data row10 col0\" >15</td>\n",
       "      <td id=\"T_7247e_row10_col1\" class=\"data row10 col1\" >0.053066</td>\n",
       "      <td id=\"T_7247e_row10_col2\" class=\"data row10 col2\" >0.193498</td>\n",
       "      <td id=\"T_7247e_row10_col3\" class=\"data row10 col3\" >0.017373</td>\n",
       "      <td id=\"T_7247e_row10_col4\" class=\"data row10 col4\" >0.263937</td>\n",
       "      <td id=\"T_7247e_row10_col5\" class=\"data row10 col5\" >68.556723</td>\n",
       "      <td id=\"T_7247e_row10_col6\" class=\"data row10 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row11_col0\" class=\"data row11 col0\" >73</td>\n",
       "      <td id=\"T_7247e_row11_col1\" class=\"data row11 col1\" >0.053224</td>\n",
       "      <td id=\"T_7247e_row11_col2\" class=\"data row11 col2\" >0.182050</td>\n",
       "      <td id=\"T_7247e_row11_col3\" class=\"data row11 col3\" >0.032496</td>\n",
       "      <td id=\"T_7247e_row11_col4\" class=\"data row11 col4\" >0.267770</td>\n",
       "      <td id=\"T_7247e_row11_col5\" class=\"data row11 col5\" >67.994763</td>\n",
       "      <td id=\"T_7247e_row11_col6\" class=\"data row11 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row12_col0\" class=\"data row12 col0\" >72</td>\n",
       "      <td id=\"T_7247e_row12_col1\" class=\"data row12 col1\" >0.056774</td>\n",
       "      <td id=\"T_7247e_row12_col2\" class=\"data row12 col2\" >0.190616</td>\n",
       "      <td id=\"T_7247e_row12_col3\" class=\"data row12 col3\" >0.025370</td>\n",
       "      <td id=\"T_7247e_row12_col4\" class=\"data row12 col4\" >0.272761</td>\n",
       "      <td id=\"T_7247e_row12_col5\" class=\"data row12 col5\" >68.710712</td>\n",
       "      <td id=\"T_7247e_row12_col6\" class=\"data row12 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row13_col0\" class=\"data row13 col0\" >37</td>\n",
       "      <td id=\"T_7247e_row13_col1\" class=\"data row13 col1\" >0.056140</td>\n",
       "      <td id=\"T_7247e_row13_col2\" class=\"data row13 col2\" >0.188700</td>\n",
       "      <td id=\"T_7247e_row13_col3\" class=\"data row13 col3\" >0.028095</td>\n",
       "      <td id=\"T_7247e_row13_col4\" class=\"data row13 col4\" >0.272936</td>\n",
       "      <td id=\"T_7247e_row13_col5\" class=\"data row13 col5\" >68.035200</td>\n",
       "      <td id=\"T_7247e_row13_col6\" class=\"data row13 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row14_col0\" class=\"data row14 col0\" >45</td>\n",
       "      <td id=\"T_7247e_row14_col1\" class=\"data row14 col1\" >0.063446</td>\n",
       "      <td id=\"T_7247e_row14_col2\" class=\"data row14 col2\" >0.180919</td>\n",
       "      <td id=\"T_7247e_row14_col3\" class=\"data row14 col3\" >0.028809</td>\n",
       "      <td id=\"T_7247e_row14_col4\" class=\"data row14 col4\" >0.273173</td>\n",
       "      <td id=\"T_7247e_row14_col5\" class=\"data row14 col5\" >68.379803</td>\n",
       "      <td id=\"T_7247e_row14_col6\" class=\"data row14 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row15_col0\" class=\"data row15 col0\" >3</td>\n",
       "      <td id=\"T_7247e_row15_col1\" class=\"data row15 col1\" >0.058104</td>\n",
       "      <td id=\"T_7247e_row15_col2\" class=\"data row15 col2\" >0.190373</td>\n",
       "      <td id=\"T_7247e_row15_col3\" class=\"data row15 col3\" >0.025841</td>\n",
       "      <td id=\"T_7247e_row15_col4\" class=\"data row15 col4\" >0.274318</td>\n",
       "      <td id=\"T_7247e_row15_col5\" class=\"data row15 col5\" >68.574487</td>\n",
       "      <td id=\"T_7247e_row15_col6\" class=\"data row15 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row16_col0\" class=\"data row16 col0\" >67</td>\n",
       "      <td id=\"T_7247e_row16_col1\" class=\"data row16 col1\" >0.053562</td>\n",
       "      <td id=\"T_7247e_row16_col2\" class=\"data row16 col2\" >0.196129</td>\n",
       "      <td id=\"T_7247e_row16_col3\" class=\"data row16 col3\" >0.025674</td>\n",
       "      <td id=\"T_7247e_row16_col4\" class=\"data row16 col4\" >0.275365</td>\n",
       "      <td id=\"T_7247e_row16_col5\" class=\"data row16 col5\" >68.310518</td>\n",
       "      <td id=\"T_7247e_row16_col6\" class=\"data row16 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row17_col0\" class=\"data row17 col0\" >69</td>\n",
       "      <td id=\"T_7247e_row17_col1\" class=\"data row17 col1\" >0.058784</td>\n",
       "      <td id=\"T_7247e_row17_col2\" class=\"data row17 col2\" >0.191430</td>\n",
       "      <td id=\"T_7247e_row17_col3\" class=\"data row17 col3\" >0.025774</td>\n",
       "      <td id=\"T_7247e_row17_col4\" class=\"data row17 col4\" >0.275988</td>\n",
       "      <td id=\"T_7247e_row17_col5\" class=\"data row17 col5\" >68.544281</td>\n",
       "      <td id=\"T_7247e_row17_col6\" class=\"data row17 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row18_col0\" class=\"data row18 col0\" >6</td>\n",
       "      <td id=\"T_7247e_row18_col1\" class=\"data row18 col1\" >0.057840</td>\n",
       "      <td id=\"T_7247e_row18_col2\" class=\"data row18 col2\" >0.195553</td>\n",
       "      <td id=\"T_7247e_row18_col3\" class=\"data row18 col3\" >0.025694</td>\n",
       "      <td id=\"T_7247e_row18_col4\" class=\"data row18 col4\" >0.279086</td>\n",
       "      <td id=\"T_7247e_row18_col5\" class=\"data row18 col5\" >68.528285</td>\n",
       "      <td id=\"T_7247e_row18_col6\" class=\"data row18 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row19_col0\" class=\"data row19 col0\" >90</td>\n",
       "      <td id=\"T_7247e_row19_col1\" class=\"data row19 col1\" >0.049104</td>\n",
       "      <td id=\"T_7247e_row19_col2\" class=\"data row19 col2\" >0.202985</td>\n",
       "      <td id=\"T_7247e_row19_col3\" class=\"data row19 col3\" >0.028445</td>\n",
       "      <td id=\"T_7247e_row19_col4\" class=\"data row19 col4\" >0.280534</td>\n",
       "      <td id=\"T_7247e_row19_col5\" class=\"data row19 col5\" >67.840712</td>\n",
       "      <td id=\"T_7247e_row19_col6\" class=\"data row19 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row20_col0\" class=\"data row20 col0\" >64</td>\n",
       "      <td id=\"T_7247e_row20_col1\" class=\"data row20 col1\" >0.055426</td>\n",
       "      <td id=\"T_7247e_row20_col2\" class=\"data row20 col2\" >0.205368</td>\n",
       "      <td id=\"T_7247e_row20_col3\" class=\"data row20 col3\" >0.024186</td>\n",
       "      <td id=\"T_7247e_row20_col4\" class=\"data row20 col4\" >0.284980</td>\n",
       "      <td id=\"T_7247e_row20_col5\" class=\"data row20 col5\" >68.698178</td>\n",
       "      <td id=\"T_7247e_row20_col6\" class=\"data row20 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row21_col0\" class=\"data row21 col0\" >60</td>\n",
       "      <td id=\"T_7247e_row21_col1\" class=\"data row21 col1\" >0.056152</td>\n",
       "      <td id=\"T_7247e_row21_col2\" class=\"data row21 col2\" >0.206769</td>\n",
       "      <td id=\"T_7247e_row21_col3\" class=\"data row21 col3\" >0.023709</td>\n",
       "      <td id=\"T_7247e_row21_col4\" class=\"data row21 col4\" >0.286630</td>\n",
       "      <td id=\"T_7247e_row21_col5\" class=\"data row21 col5\" >68.658572</td>\n",
       "      <td id=\"T_7247e_row21_col6\" class=\"data row21 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row22_col0\" class=\"data row22 col0\" >43</td>\n",
       "      <td id=\"T_7247e_row22_col1\" class=\"data row22 col1\" >0.056351</td>\n",
       "      <td id=\"T_7247e_row22_col2\" class=\"data row22 col2\" >0.211394</td>\n",
       "      <td id=\"T_7247e_row22_col3\" class=\"data row22 col3\" >0.021583</td>\n",
       "      <td id=\"T_7247e_row22_col4\" class=\"data row22 col4\" >0.289328</td>\n",
       "      <td id=\"T_7247e_row22_col5\" class=\"data row22 col5\" >68.520120</td>\n",
       "      <td id=\"T_7247e_row22_col6\" class=\"data row22 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row23_col0\" class=\"data row23 col0\" >52</td>\n",
       "      <td id=\"T_7247e_row23_col1\" class=\"data row23 col1\" >0.058451</td>\n",
       "      <td id=\"T_7247e_row23_col2\" class=\"data row23 col2\" >0.206273</td>\n",
       "      <td id=\"T_7247e_row23_col3\" class=\"data row23 col3\" >0.025501</td>\n",
       "      <td id=\"T_7247e_row23_col4\" class=\"data row23 col4\" >0.290225</td>\n",
       "      <td id=\"T_7247e_row23_col5\" class=\"data row23 col5\" >68.450284</td>\n",
       "      <td id=\"T_7247e_row23_col6\" class=\"data row23 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row24_col0\" class=\"data row24 col0\" >46</td>\n",
       "      <td id=\"T_7247e_row24_col1\" class=\"data row24 col1\" >0.056757</td>\n",
       "      <td id=\"T_7247e_row24_col2\" class=\"data row24 col2\" >0.209465</td>\n",
       "      <td id=\"T_7247e_row24_col3\" class=\"data row24 col3\" >0.031921</td>\n",
       "      <td id=\"T_7247e_row24_col4\" class=\"data row24 col4\" >0.298143</td>\n",
       "      <td id=\"T_7247e_row24_col5\" class=\"data row24 col5\" >71.229079</td>\n",
       "      <td id=\"T_7247e_row24_col6\" class=\"data row24 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row25_col0\" class=\"data row25 col0\" >78</td>\n",
       "      <td id=\"T_7247e_row25_col1\" class=\"data row25 col1\" >0.045168</td>\n",
       "      <td id=\"T_7247e_row25_col2\" class=\"data row25 col2\" >0.234188</td>\n",
       "      <td id=\"T_7247e_row25_col3\" class=\"data row25 col3\" >0.019534</td>\n",
       "      <td id=\"T_7247e_row25_col4\" class=\"data row25 col4\" >0.298890</td>\n",
       "      <td id=\"T_7247e_row25_col5\" class=\"data row25 col5\" >68.183671</td>\n",
       "      <td id=\"T_7247e_row25_col6\" class=\"data row25 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row26_col0\" class=\"data row26 col0\" >33</td>\n",
       "      <td id=\"T_7247e_row26_col1\" class=\"data row26 col1\" >0.049005</td>\n",
       "      <td id=\"T_7247e_row26_col2\" class=\"data row26 col2\" >0.232485</td>\n",
       "      <td id=\"T_7247e_row26_col3\" class=\"data row26 col3\" >0.020466</td>\n",
       "      <td id=\"T_7247e_row26_col4\" class=\"data row26 col4\" >0.301956</td>\n",
       "      <td id=\"T_7247e_row26_col5\" class=\"data row26 col5\" >67.816796</td>\n",
       "      <td id=\"T_7247e_row26_col6\" class=\"data row26 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row27_col0\" class=\"data row27 col0\" >75</td>\n",
       "      <td id=\"T_7247e_row27_col1\" class=\"data row27 col1\" >0.059338</td>\n",
       "      <td id=\"T_7247e_row27_col2\" class=\"data row27 col2\" >0.214782</td>\n",
       "      <td id=\"T_7247e_row27_col3\" class=\"data row27 col3\" >0.029162</td>\n",
       "      <td id=\"T_7247e_row27_col4\" class=\"data row27 col4\" >0.303283</td>\n",
       "      <td id=\"T_7247e_row27_col5\" class=\"data row27 col5\" >67.656625</td>\n",
       "      <td id=\"T_7247e_row27_col6\" class=\"data row27 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row28_col0\" class=\"data row28 col0\" >30</td>\n",
       "      <td id=\"T_7247e_row28_col1\" class=\"data row28 col1\" >0.056285</td>\n",
       "      <td id=\"T_7247e_row28_col2\" class=\"data row28 col2\" >0.228212</td>\n",
       "      <td id=\"T_7247e_row28_col3\" class=\"data row28 col3\" >0.020477</td>\n",
       "      <td id=\"T_7247e_row28_col4\" class=\"data row28 col4\" >0.304974</td>\n",
       "      <td id=\"T_7247e_row28_col5\" class=\"data row28 col5\" >68.623696</td>\n",
       "      <td id=\"T_7247e_row28_col6\" class=\"data row28 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row29_col0\" class=\"data row29 col0\" >7</td>\n",
       "      <td id=\"T_7247e_row29_col1\" class=\"data row29 col1\" >0.054799</td>\n",
       "      <td id=\"T_7247e_row29_col2\" class=\"data row29 col2\" >0.230781</td>\n",
       "      <td id=\"T_7247e_row29_col3\" class=\"data row29 col3\" >0.020172</td>\n",
       "      <td id=\"T_7247e_row29_col4\" class=\"data row29 col4\" >0.305752</td>\n",
       "      <td id=\"T_7247e_row29_col5\" class=\"data row29 col5\" >68.462136</td>\n",
       "      <td id=\"T_7247e_row29_col6\" class=\"data row29 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row30_col0\" class=\"data row30 col0\" >12</td>\n",
       "      <td id=\"T_7247e_row30_col1\" class=\"data row30 col1\" >0.049422</td>\n",
       "      <td id=\"T_7247e_row30_col2\" class=\"data row30 col2\" >0.239511</td>\n",
       "      <td id=\"T_7247e_row30_col3\" class=\"data row30 col3\" >0.020974</td>\n",
       "      <td id=\"T_7247e_row30_col4\" class=\"data row30 col4\" >0.309907</td>\n",
       "      <td id=\"T_7247e_row30_col5\" class=\"data row30 col5\" >66.268271</td>\n",
       "      <td id=\"T_7247e_row30_col6\" class=\"data row30 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row31_col0\" class=\"data row31 col0\" >99</td>\n",
       "      <td id=\"T_7247e_row31_col1\" class=\"data row31 col1\" >0.050310</td>\n",
       "      <td id=\"T_7247e_row31_col2\" class=\"data row31 col2\" >0.240533</td>\n",
       "      <td id=\"T_7247e_row31_col3\" class=\"data row31 col3\" >0.022123</td>\n",
       "      <td id=\"T_7247e_row31_col4\" class=\"data row31 col4\" >0.312966</td>\n",
       "      <td id=\"T_7247e_row31_col5\" class=\"data row31 col5\" >68.948193</td>\n",
       "      <td id=\"T_7247e_row31_col6\" class=\"data row31 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row32_col0\" class=\"data row32 col0\" >95</td>\n",
       "      <td id=\"T_7247e_row32_col1\" class=\"data row32 col1\" >0.049129</td>\n",
       "      <td id=\"T_7247e_row32_col2\" class=\"data row32 col2\" >0.244308</td>\n",
       "      <td id=\"T_7247e_row32_col3\" class=\"data row32 col3\" >0.021311</td>\n",
       "      <td id=\"T_7247e_row32_col4\" class=\"data row32 col4\" >0.314747</td>\n",
       "      <td id=\"T_7247e_row32_col5\" class=\"data row32 col5\" >67.772573</td>\n",
       "      <td id=\"T_7247e_row32_col6\" class=\"data row32 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row33_col0\" class=\"data row33 col0\" >97</td>\n",
       "      <td id=\"T_7247e_row33_col1\" class=\"data row33 col1\" >0.059988</td>\n",
       "      <td id=\"T_7247e_row33_col2\" class=\"data row33 col2\" >0.233952</td>\n",
       "      <td id=\"T_7247e_row33_col3\" class=\"data row33 col3\" >0.024485</td>\n",
       "      <td id=\"T_7247e_row33_col4\" class=\"data row33 col4\" >0.318425</td>\n",
       "      <td id=\"T_7247e_row33_col5\" class=\"data row33 col5\" >68.183401</td>\n",
       "      <td id=\"T_7247e_row33_col6\" class=\"data row33 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row34_col0\" class=\"data row34 col0\" >26</td>\n",
       "      <td id=\"T_7247e_row34_col1\" class=\"data row34 col1\" >0.061521</td>\n",
       "      <td id=\"T_7247e_row34_col2\" class=\"data row34 col2\" >0.229832</td>\n",
       "      <td id=\"T_7247e_row34_col3\" class=\"data row34 col3\" >0.027638</td>\n",
       "      <td id=\"T_7247e_row34_col4\" class=\"data row34 col4\" >0.318992</td>\n",
       "      <td id=\"T_7247e_row34_col5\" class=\"data row34 col5\" >66.315274</td>\n",
       "      <td id=\"T_7247e_row34_col6\" class=\"data row34 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row35_col0\" class=\"data row35 col0\" >17</td>\n",
       "      <td id=\"T_7247e_row35_col1\" class=\"data row35 col1\" >0.049923</td>\n",
       "      <td id=\"T_7247e_row35_col2\" class=\"data row35 col2\" >0.253764</td>\n",
       "      <td id=\"T_7247e_row35_col3\" class=\"data row35 col3\" >0.016590</td>\n",
       "      <td id=\"T_7247e_row35_col4\" class=\"data row35 col4\" >0.320277</td>\n",
       "      <td id=\"T_7247e_row35_col5\" class=\"data row35 col5\" >68.463317</td>\n",
       "      <td id=\"T_7247e_row35_col6\" class=\"data row35 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row36_col0\" class=\"data row36 col0\" >71</td>\n",
       "      <td id=\"T_7247e_row36_col1\" class=\"data row36 col1\" >0.048339</td>\n",
       "      <td id=\"T_7247e_row36_col2\" class=\"data row36 col2\" >0.255144</td>\n",
       "      <td id=\"T_7247e_row36_col3\" class=\"data row36 col3\" >0.019260</td>\n",
       "      <td id=\"T_7247e_row36_col4\" class=\"data row36 col4\" >0.322743</td>\n",
       "      <td id=\"T_7247e_row36_col5\" class=\"data row36 col5\" >68.356094</td>\n",
       "      <td id=\"T_7247e_row36_col6\" class=\"data row36 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row37_col0\" class=\"data row37 col0\" >56</td>\n",
       "      <td id=\"T_7247e_row37_col1\" class=\"data row37 col1\" >0.053764</td>\n",
       "      <td id=\"T_7247e_row37_col2\" class=\"data row37 col2\" >0.248249</td>\n",
       "      <td id=\"T_7247e_row37_col3\" class=\"data row37 col3\" >0.021306</td>\n",
       "      <td id=\"T_7247e_row37_col4\" class=\"data row37 col4\" >0.323318</td>\n",
       "      <td id=\"T_7247e_row37_col5\" class=\"data row37 col5\" >68.332258</td>\n",
       "      <td id=\"T_7247e_row37_col6\" class=\"data row37 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row38_col0\" class=\"data row38 col0\" >54</td>\n",
       "      <td id=\"T_7247e_row38_col1\" class=\"data row38 col1\" >0.055414</td>\n",
       "      <td id=\"T_7247e_row38_col2\" class=\"data row38 col2\" >0.246075</td>\n",
       "      <td id=\"T_7247e_row38_col3\" class=\"data row38 col3\" >0.022427</td>\n",
       "      <td id=\"T_7247e_row38_col4\" class=\"data row38 col4\" >0.323915</td>\n",
       "      <td id=\"T_7247e_row38_col5\" class=\"data row38 col5\" >68.471910</td>\n",
       "      <td id=\"T_7247e_row38_col6\" class=\"data row38 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row39_col0\" class=\"data row39 col0\" >96</td>\n",
       "      <td id=\"T_7247e_row39_col1\" class=\"data row39 col1\" >0.056215</td>\n",
       "      <td id=\"T_7247e_row39_col2\" class=\"data row39 col2\" >0.242505</td>\n",
       "      <td id=\"T_7247e_row39_col3\" class=\"data row39 col3\" >0.029601</td>\n",
       "      <td id=\"T_7247e_row39_col4\" class=\"data row39 col4\" >0.328321</td>\n",
       "      <td id=\"T_7247e_row39_col5\" class=\"data row39 col5\" >68.260187</td>\n",
       "      <td id=\"T_7247e_row39_col6\" class=\"data row39 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row40_col0\" class=\"data row40 col0\" >92</td>\n",
       "      <td id=\"T_7247e_row40_col1\" class=\"data row40 col1\" >0.056777</td>\n",
       "      <td id=\"T_7247e_row40_col2\" class=\"data row40 col2\" >0.252286</td>\n",
       "      <td id=\"T_7247e_row40_col3\" class=\"data row40 col3\" >0.022007</td>\n",
       "      <td id=\"T_7247e_row40_col4\" class=\"data row40 col4\" >0.331070</td>\n",
       "      <td id=\"T_7247e_row40_col5\" class=\"data row40 col5\" >68.493473</td>\n",
       "      <td id=\"T_7247e_row40_col6\" class=\"data row40 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row41_col0\" class=\"data row41 col0\" >35</td>\n",
       "      <td id=\"T_7247e_row41_col1\" class=\"data row41 col1\" >0.059935</td>\n",
       "      <td id=\"T_7247e_row41_col2\" class=\"data row41 col2\" >0.244818</td>\n",
       "      <td id=\"T_7247e_row41_col3\" class=\"data row41 col3\" >0.027673</td>\n",
       "      <td id=\"T_7247e_row41_col4\" class=\"data row41 col4\" >0.332426</td>\n",
       "      <td id=\"T_7247e_row41_col5\" class=\"data row41 col5\" >68.531205</td>\n",
       "      <td id=\"T_7247e_row41_col6\" class=\"data row41 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row42_col0\" class=\"data row42 col0\" >84</td>\n",
       "      <td id=\"T_7247e_row42_col1\" class=\"data row42 col1\" >0.054529</td>\n",
       "      <td id=\"T_7247e_row42_col2\" class=\"data row42 col2\" >0.256154</td>\n",
       "      <td id=\"T_7247e_row42_col3\" class=\"data row42 col3\" >0.023658</td>\n",
       "      <td id=\"T_7247e_row42_col4\" class=\"data row42 col4\" >0.334342</td>\n",
       "      <td id=\"T_7247e_row42_col5\" class=\"data row42 col5\" >68.502741</td>\n",
       "      <td id=\"T_7247e_row42_col6\" class=\"data row42 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row43_col0\" class=\"data row43 col0\" >21</td>\n",
       "      <td id=\"T_7247e_row43_col1\" class=\"data row43 col1\" >0.045742</td>\n",
       "      <td id=\"T_7247e_row43_col2\" class=\"data row43 col2\" >0.275328</td>\n",
       "      <td id=\"T_7247e_row43_col3\" class=\"data row43 col3\" >0.018537</td>\n",
       "      <td id=\"T_7247e_row43_col4\" class=\"data row43 col4\" >0.339607</td>\n",
       "      <td id=\"T_7247e_row43_col5\" class=\"data row43 col5\" >68.445772</td>\n",
       "      <td id=\"T_7247e_row43_col6\" class=\"data row43 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row44_col0\" class=\"data row44 col0\" >55</td>\n",
       "      <td id=\"T_7247e_row44_col1\" class=\"data row44 col1\" >0.053368</td>\n",
       "      <td id=\"T_7247e_row44_col2\" class=\"data row44 col2\" >0.271209</td>\n",
       "      <td id=\"T_7247e_row44_col3\" class=\"data row44 col3\" >0.018066</td>\n",
       "      <td id=\"T_7247e_row44_col4\" class=\"data row44 col4\" >0.342643</td>\n",
       "      <td id=\"T_7247e_row44_col5\" class=\"data row44 col5\" >68.496166</td>\n",
       "      <td id=\"T_7247e_row44_col6\" class=\"data row44 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row45_col0\" class=\"data row45 col0\" >32</td>\n",
       "      <td id=\"T_7247e_row45_col1\" class=\"data row45 col1\" >0.053034</td>\n",
       "      <td id=\"T_7247e_row45_col2\" class=\"data row45 col2\" >0.266511</td>\n",
       "      <td id=\"T_7247e_row45_col3\" class=\"data row45 col3\" >0.024008</td>\n",
       "      <td id=\"T_7247e_row45_col4\" class=\"data row45 col4\" >0.343553</td>\n",
       "      <td id=\"T_7247e_row45_col5\" class=\"data row45 col5\" >68.273320</td>\n",
       "      <td id=\"T_7247e_row45_col6\" class=\"data row45 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row46_col0\" class=\"data row46 col0\" >39</td>\n",
       "      <td id=\"T_7247e_row46_col1\" class=\"data row46 col1\" >0.047781</td>\n",
       "      <td id=\"T_7247e_row46_col2\" class=\"data row46 col2\" >0.277955</td>\n",
       "      <td id=\"T_7247e_row46_col3\" class=\"data row46 col3\" >0.020285</td>\n",
       "      <td id=\"T_7247e_row46_col4\" class=\"data row46 col4\" >0.346022</td>\n",
       "      <td id=\"T_7247e_row46_col5\" class=\"data row46 col5\" >68.473385</td>\n",
       "      <td id=\"T_7247e_row46_col6\" class=\"data row46 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row47_col0\" class=\"data row47 col0\" >61</td>\n",
       "      <td id=\"T_7247e_row47_col1\" class=\"data row47 col1\" >0.043975</td>\n",
       "      <td id=\"T_7247e_row47_col2\" class=\"data row47 col2\" >0.278165</td>\n",
       "      <td id=\"T_7247e_row47_col3\" class=\"data row47 col3\" >0.025861</td>\n",
       "      <td id=\"T_7247e_row47_col4\" class=\"data row47 col4\" >0.348002</td>\n",
       "      <td id=\"T_7247e_row47_col5\" class=\"data row47 col5\" >67.455558</td>\n",
       "      <td id=\"T_7247e_row47_col6\" class=\"data row47 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row48_col0\" class=\"data row48 col0\" >28</td>\n",
       "      <td id=\"T_7247e_row48_col1\" class=\"data row48 col1\" >0.056308</td>\n",
       "      <td id=\"T_7247e_row48_col2\" class=\"data row48 col2\" >0.265572</td>\n",
       "      <td id=\"T_7247e_row48_col3\" class=\"data row48 col3\" >0.029332</td>\n",
       "      <td id=\"T_7247e_row48_col4\" class=\"data row48 col4\" >0.351213</td>\n",
       "      <td id=\"T_7247e_row48_col5\" class=\"data row48 col5\" >68.523097</td>\n",
       "      <td id=\"T_7247e_row48_col6\" class=\"data row48 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row49_col0\" class=\"data row49 col0\" >19</td>\n",
       "      <td id=\"T_7247e_row49_col1\" class=\"data row49 col1\" >0.046408</td>\n",
       "      <td id=\"T_7247e_row49_col2\" class=\"data row49 col2\" >0.285895</td>\n",
       "      <td id=\"T_7247e_row49_col3\" class=\"data row49 col3\" >0.019085</td>\n",
       "      <td id=\"T_7247e_row49_col4\" class=\"data row49 col4\" >0.351388</td>\n",
       "      <td id=\"T_7247e_row49_col5\" class=\"data row49 col5\" >68.512391</td>\n",
       "      <td id=\"T_7247e_row49_col6\" class=\"data row49 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row50_col0\" class=\"data row50 col0\" >38</td>\n",
       "      <td id=\"T_7247e_row50_col1\" class=\"data row50 col1\" >0.043819</td>\n",
       "      <td id=\"T_7247e_row50_col2\" class=\"data row50 col2\" >0.277794</td>\n",
       "      <td id=\"T_7247e_row50_col3\" class=\"data row50 col3\" >0.030401</td>\n",
       "      <td id=\"T_7247e_row50_col4\" class=\"data row50 col4\" >0.352015</td>\n",
       "      <td id=\"T_7247e_row50_col5\" class=\"data row50 col5\" >66.409670</td>\n",
       "      <td id=\"T_7247e_row50_col6\" class=\"data row50 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row51_col0\" class=\"data row51 col0\" >80</td>\n",
       "      <td id=\"T_7247e_row51_col1\" class=\"data row51 col1\" >0.051543</td>\n",
       "      <td id=\"T_7247e_row51_col2\" class=\"data row51 col2\" >0.283279</td>\n",
       "      <td id=\"T_7247e_row51_col3\" class=\"data row51 col3\" >0.020094</td>\n",
       "      <td id=\"T_7247e_row51_col4\" class=\"data row51 col4\" >0.354916</td>\n",
       "      <td id=\"T_7247e_row51_col5\" class=\"data row51 col5\" >68.552513</td>\n",
       "      <td id=\"T_7247e_row51_col6\" class=\"data row51 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row52_col0\" class=\"data row52 col0\" >22</td>\n",
       "      <td id=\"T_7247e_row52_col1\" class=\"data row52 col1\" >0.042825</td>\n",
       "      <td id=\"T_7247e_row52_col2\" class=\"data row52 col2\" >0.289642</td>\n",
       "      <td id=\"T_7247e_row52_col3\" class=\"data row52 col3\" >0.022635</td>\n",
       "      <td id=\"T_7247e_row52_col4\" class=\"data row52 col4\" >0.355102</td>\n",
       "      <td id=\"T_7247e_row52_col5\" class=\"data row52 col5\" >68.397202</td>\n",
       "      <td id=\"T_7247e_row52_col6\" class=\"data row52 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row53_col0\" class=\"data row53 col0\" >62</td>\n",
       "      <td id=\"T_7247e_row53_col1\" class=\"data row53 col1\" >0.052687</td>\n",
       "      <td id=\"T_7247e_row53_col2\" class=\"data row53 col2\" >0.286332</td>\n",
       "      <td id=\"T_7247e_row53_col3\" class=\"data row53 col3\" >0.016798</td>\n",
       "      <td id=\"T_7247e_row53_col4\" class=\"data row53 col4\" >0.355817</td>\n",
       "      <td id=\"T_7247e_row53_col5\" class=\"data row53 col5\" >68.547235</td>\n",
       "      <td id=\"T_7247e_row53_col6\" class=\"data row53 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row54_col0\" class=\"data row54 col0\" >51</td>\n",
       "      <td id=\"T_7247e_row54_col1\" class=\"data row54 col1\" >0.056969</td>\n",
       "      <td id=\"T_7247e_row54_col2\" class=\"data row54 col2\" >0.273856</td>\n",
       "      <td id=\"T_7247e_row54_col3\" class=\"data row54 col3\" >0.025651</td>\n",
       "      <td id=\"T_7247e_row54_col4\" class=\"data row54 col4\" >0.356476</td>\n",
       "      <td id=\"T_7247e_row54_col5\" class=\"data row54 col5\" >68.464066</td>\n",
       "      <td id=\"T_7247e_row54_col6\" class=\"data row54 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row55_col0\" class=\"data row55 col0\" >94</td>\n",
       "      <td id=\"T_7247e_row55_col1\" class=\"data row55 col1\" >0.052617</td>\n",
       "      <td id=\"T_7247e_row55_col2\" class=\"data row55 col2\" >0.278732</td>\n",
       "      <td id=\"T_7247e_row55_col3\" class=\"data row55 col3\" >0.025768</td>\n",
       "      <td id=\"T_7247e_row55_col4\" class=\"data row55 col4\" >0.357117</td>\n",
       "      <td id=\"T_7247e_row55_col5\" class=\"data row55 col5\" >68.205924</td>\n",
       "      <td id=\"T_7247e_row55_col6\" class=\"data row55 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row56_col0\" class=\"data row56 col0\" >87</td>\n",
       "      <td id=\"T_7247e_row56_col1\" class=\"data row56 col1\" >0.048517</td>\n",
       "      <td id=\"T_7247e_row56_col2\" class=\"data row56 col2\" >0.292186</td>\n",
       "      <td id=\"T_7247e_row56_col3\" class=\"data row56 col3\" >0.018707</td>\n",
       "      <td id=\"T_7247e_row56_col4\" class=\"data row56 col4\" >0.359410</td>\n",
       "      <td id=\"T_7247e_row56_col5\" class=\"data row56 col5\" >68.434467</td>\n",
       "      <td id=\"T_7247e_row56_col6\" class=\"data row56 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row57_col0\" class=\"data row57 col0\" >10</td>\n",
       "      <td id=\"T_7247e_row57_col1\" class=\"data row57 col1\" >0.049404</td>\n",
       "      <td id=\"T_7247e_row57_col2\" class=\"data row57 col2\" >0.294561</td>\n",
       "      <td id=\"T_7247e_row57_col3\" class=\"data row57 col3\" >0.017423</td>\n",
       "      <td id=\"T_7247e_row57_col4\" class=\"data row57 col4\" >0.361388</td>\n",
       "      <td id=\"T_7247e_row57_col5\" class=\"data row57 col5\" >68.449345</td>\n",
       "      <td id=\"T_7247e_row57_col6\" class=\"data row57 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row58_col0\" class=\"data row58 col0\" >14</td>\n",
       "      <td id=\"T_7247e_row58_col1\" class=\"data row58 col1\" >0.041117</td>\n",
       "      <td id=\"T_7247e_row58_col2\" class=\"data row58 col2\" >0.301343</td>\n",
       "      <td id=\"T_7247e_row58_col3\" class=\"data row58 col3\" >0.019974</td>\n",
       "      <td id=\"T_7247e_row58_col4\" class=\"data row58 col4\" >0.362434</td>\n",
       "      <td id=\"T_7247e_row58_col5\" class=\"data row58 col5\" >68.525798</td>\n",
       "      <td id=\"T_7247e_row58_col6\" class=\"data row58 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row59_col0\" class=\"data row59 col0\" >8</td>\n",
       "      <td id=\"T_7247e_row59_col1\" class=\"data row59 col1\" >0.047769</td>\n",
       "      <td id=\"T_7247e_row59_col2\" class=\"data row59 col2\" >0.297923</td>\n",
       "      <td id=\"T_7247e_row59_col3\" class=\"data row59 col3\" >0.018566</td>\n",
       "      <td id=\"T_7247e_row59_col4\" class=\"data row59 col4\" >0.364258</td>\n",
       "      <td id=\"T_7247e_row59_col5\" class=\"data row59 col5\" >68.557410</td>\n",
       "      <td id=\"T_7247e_row59_col6\" class=\"data row59 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row60_col0\" class=\"data row60 col0\" >48</td>\n",
       "      <td id=\"T_7247e_row60_col1\" class=\"data row60 col1\" >0.048114</td>\n",
       "      <td id=\"T_7247e_row60_col2\" class=\"data row60 col2\" >0.297994</td>\n",
       "      <td id=\"T_7247e_row60_col3\" class=\"data row60 col3\" >0.018709</td>\n",
       "      <td id=\"T_7247e_row60_col4\" class=\"data row60 col4\" >0.364817</td>\n",
       "      <td id=\"T_7247e_row60_col5\" class=\"data row60 col5\" >68.152472</td>\n",
       "      <td id=\"T_7247e_row60_col6\" class=\"data row60 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row61_col0\" class=\"data row61 col0\" >70</td>\n",
       "      <td id=\"T_7247e_row61_col1\" class=\"data row61 col1\" >0.048022</td>\n",
       "      <td id=\"T_7247e_row61_col2\" class=\"data row61 col2\" >0.315296</td>\n",
       "      <td id=\"T_7247e_row61_col3\" class=\"data row61 col3\" >0.020005</td>\n",
       "      <td id=\"T_7247e_row61_col4\" class=\"data row61 col4\" >0.383322</td>\n",
       "      <td id=\"T_7247e_row61_col5\" class=\"data row61 col5\" >68.456714</td>\n",
       "      <td id=\"T_7247e_row61_col6\" class=\"data row61 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row62_col0\" class=\"data row62 col0\" >23</td>\n",
       "      <td id=\"T_7247e_row62_col1\" class=\"data row62 col1\" >0.062868</td>\n",
       "      <td id=\"T_7247e_row62_col2\" class=\"data row62 col2\" >0.311929</td>\n",
       "      <td id=\"T_7247e_row62_col3\" class=\"data row62 col3\" >0.018944</td>\n",
       "      <td id=\"T_7247e_row62_col4\" class=\"data row62 col4\" >0.393740</td>\n",
       "      <td id=\"T_7247e_row62_col5\" class=\"data row62 col5\" >68.405972</td>\n",
       "      <td id=\"T_7247e_row62_col6\" class=\"data row62 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row63_col0\" class=\"data row63 col0\" >20</td>\n",
       "      <td id=\"T_7247e_row63_col1\" class=\"data row63 col1\" >0.075873</td>\n",
       "      <td id=\"T_7247e_row63_col2\" class=\"data row63 col2\" >0.300241</td>\n",
       "      <td id=\"T_7247e_row63_col3\" class=\"data row63 col3\" >0.019939</td>\n",
       "      <td id=\"T_7247e_row63_col4\" class=\"data row63 col4\" >0.396053</td>\n",
       "      <td id=\"T_7247e_row63_col5\" class=\"data row63 col5\" >68.417017</td>\n",
       "      <td id=\"T_7247e_row63_col6\" class=\"data row63 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row64_col0\" class=\"data row64 col0\" >42</td>\n",
       "      <td id=\"T_7247e_row64_col1\" class=\"data row64 col1\" >0.077039</td>\n",
       "      <td id=\"T_7247e_row64_col2\" class=\"data row64 col2\" >0.298184</td>\n",
       "      <td id=\"T_7247e_row64_col3\" class=\"data row64 col3\" >0.021054</td>\n",
       "      <td id=\"T_7247e_row64_col4\" class=\"data row64 col4\" >0.396277</td>\n",
       "      <td id=\"T_7247e_row64_col5\" class=\"data row64 col5\" >68.280266</td>\n",
       "      <td id=\"T_7247e_row64_col6\" class=\"data row64 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row65_col0\" class=\"data row65 col0\" >59</td>\n",
       "      <td id=\"T_7247e_row65_col1\" class=\"data row65 col1\" >0.048641</td>\n",
       "      <td id=\"T_7247e_row65_col2\" class=\"data row65 col2\" >0.285145</td>\n",
       "      <td id=\"T_7247e_row65_col3\" class=\"data row65 col3\" >0.065848</td>\n",
       "      <td id=\"T_7247e_row65_col4\" class=\"data row65 col4\" >0.399634</td>\n",
       "      <td id=\"T_7247e_row65_col5\" class=\"data row65 col5\" >65.373918</td>\n",
       "      <td id=\"T_7247e_row65_col6\" class=\"data row65 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row66_col0\" class=\"data row66 col0\" >81</td>\n",
       "      <td id=\"T_7247e_row66_col1\" class=\"data row66 col1\" >0.079397</td>\n",
       "      <td id=\"T_7247e_row66_col2\" class=\"data row66 col2\" >0.323954</td>\n",
       "      <td id=\"T_7247e_row66_col3\" class=\"data row66 col3\" >0.020041</td>\n",
       "      <td id=\"T_7247e_row66_col4\" class=\"data row66 col4\" >0.423392</td>\n",
       "      <td id=\"T_7247e_row66_col5\" class=\"data row66 col5\" >68.411735</td>\n",
       "      <td id=\"T_7247e_row66_col6\" class=\"data row66 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row67_col0\" class=\"data row67 col0\" >66</td>\n",
       "      <td id=\"T_7247e_row67_col1\" class=\"data row67 col1\" >0.098157</td>\n",
       "      <td id=\"T_7247e_row67_col2\" class=\"data row67 col2\" >0.326186</td>\n",
       "      <td id=\"T_7247e_row67_col3\" class=\"data row67 col3\" >0.024883</td>\n",
       "      <td id=\"T_7247e_row67_col4\" class=\"data row67 col4\" >0.449226</td>\n",
       "      <td id=\"T_7247e_row67_col5\" class=\"data row67 col5\" >68.387005</td>\n",
       "      <td id=\"T_7247e_row67_col6\" class=\"data row67 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row68_col0\" class=\"data row68 col0\" >86</td>\n",
       "      <td id=\"T_7247e_row68_col1\" class=\"data row68 col1\" >0.090302</td>\n",
       "      <td id=\"T_7247e_row68_col2\" class=\"data row68 col2\" >0.377994</td>\n",
       "      <td id=\"T_7247e_row68_col3\" class=\"data row68 col3\" >0.024016</td>\n",
       "      <td id=\"T_7247e_row68_col4\" class=\"data row68 col4\" >0.492313</td>\n",
       "      <td id=\"T_7247e_row68_col5\" class=\"data row68 col5\" >68.367355</td>\n",
       "      <td id=\"T_7247e_row68_col6\" class=\"data row68 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row69_col0\" class=\"data row69 col0\" >65</td>\n",
       "      <td id=\"T_7247e_row69_col1\" class=\"data row69 col1\" >0.107231</td>\n",
       "      <td id=\"T_7247e_row69_col2\" class=\"data row69 col2\" >0.362882</td>\n",
       "      <td id=\"T_7247e_row69_col3\" class=\"data row69 col3\" >0.023502</td>\n",
       "      <td id=\"T_7247e_row69_col4\" class=\"data row69 col4\" >0.493615</td>\n",
       "      <td id=\"T_7247e_row69_col5\" class=\"data row69 col5\" >68.324253</td>\n",
       "      <td id=\"T_7247e_row69_col6\" class=\"data row69 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row70_col0\" class=\"data row70 col0\" >18</td>\n",
       "      <td id=\"T_7247e_row70_col1\" class=\"data row70 col1\" >0.124412</td>\n",
       "      <td id=\"T_7247e_row70_col2\" class=\"data row70 col2\" >0.347981</td>\n",
       "      <td id=\"T_7247e_row70_col3\" class=\"data row70 col3\" >0.027023</td>\n",
       "      <td id=\"T_7247e_row70_col4\" class=\"data row70 col4\" >0.499416</td>\n",
       "      <td id=\"T_7247e_row70_col5\" class=\"data row70 col5\" >68.442922</td>\n",
       "      <td id=\"T_7247e_row70_col6\" class=\"data row70 col6\" >Ipopt 3.14.16\\x3a Solved To Acceptable Level.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row71_col0\" class=\"data row71 col0\" >49</td>\n",
       "      <td id=\"T_7247e_row71_col1\" class=\"data row71 col1\" >0.118232</td>\n",
       "      <td id=\"T_7247e_row71_col2\" class=\"data row71 col2\" >0.358485</td>\n",
       "      <td id=\"T_7247e_row71_col3\" class=\"data row71 col3\" >0.025710</td>\n",
       "      <td id=\"T_7247e_row71_col4\" class=\"data row71 col4\" >0.502427</td>\n",
       "      <td id=\"T_7247e_row71_col5\" class=\"data row71 col5\" >67.754980</td>\n",
       "      <td id=\"T_7247e_row71_col6\" class=\"data row71 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row72_col0\" class=\"data row72 col0\" >9</td>\n",
       "      <td id=\"T_7247e_row72_col1\" class=\"data row72 col1\" >0.114534</td>\n",
       "      <td id=\"T_7247e_row72_col2\" class=\"data row72 col2\" >0.367212</td>\n",
       "      <td id=\"T_7247e_row72_col3\" class=\"data row72 col3\" >0.028592</td>\n",
       "      <td id=\"T_7247e_row72_col4\" class=\"data row72 col4\" >0.510338</td>\n",
       "      <td id=\"T_7247e_row72_col5\" class=\"data row72 col5\" >68.048715</td>\n",
       "      <td id=\"T_7247e_row72_col6\" class=\"data row72 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row73_col0\" class=\"data row73 col0\" >53</td>\n",
       "      <td id=\"T_7247e_row73_col1\" class=\"data row73 col1\" >0.094160</td>\n",
       "      <td id=\"T_7247e_row73_col2\" class=\"data row73 col2\" >0.408606</td>\n",
       "      <td id=\"T_7247e_row73_col3\" class=\"data row73 col3\" >0.023068</td>\n",
       "      <td id=\"T_7247e_row73_col4\" class=\"data row73 col4\" >0.525834</td>\n",
       "      <td id=\"T_7247e_row73_col5\" class=\"data row73 col5\" >68.426091</td>\n",
       "      <td id=\"T_7247e_row73_col6\" class=\"data row73 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row74_col0\" class=\"data row74 col0\" >76</td>\n",
       "      <td id=\"T_7247e_row74_col1\" class=\"data row74 col1\" >0.254195</td>\n",
       "      <td id=\"T_7247e_row74_col2\" class=\"data row74 col2\" >0.454396</td>\n",
       "      <td id=\"T_7247e_row74_col3\" class=\"data row74 col3\" >0.035866</td>\n",
       "      <td id=\"T_7247e_row74_col4\" class=\"data row74 col4\" >0.744457</td>\n",
       "      <td id=\"T_7247e_row74_col5\" class=\"data row74 col5\" >68.362288</td>\n",
       "      <td id=\"T_7247e_row74_col6\" class=\"data row74 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row75_col0\" class=\"data row75 col0\" >41</td>\n",
       "      <td id=\"T_7247e_row75_col1\" class=\"data row75 col1\" >0.258446</td>\n",
       "      <td id=\"T_7247e_row75_col2\" class=\"data row75 col2\" >0.458091</td>\n",
       "      <td id=\"T_7247e_row75_col3\" class=\"data row75 col3\" >0.041302</td>\n",
       "      <td id=\"T_7247e_row75_col4\" class=\"data row75 col4\" >0.757840</td>\n",
       "      <td id=\"T_7247e_row75_col5\" class=\"data row75 col5\" >68.138641</td>\n",
       "      <td id=\"T_7247e_row75_col6\" class=\"data row75 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row76_col0\" class=\"data row76 col0\" >91</td>\n",
       "      <td id=\"T_7247e_row76_col1\" class=\"data row76 col1\" >0.217456</td>\n",
       "      <td id=\"T_7247e_row76_col2\" class=\"data row76 col2\" >0.505432</td>\n",
       "      <td id=\"T_7247e_row76_col3\" class=\"data row76 col3\" >0.081960</td>\n",
       "      <td id=\"T_7247e_row76_col4\" class=\"data row76 col4\" >0.804848</td>\n",
       "      <td id=\"T_7247e_row76_col5\" class=\"data row76 col5\" >66.839777</td>\n",
       "      <td id=\"T_7247e_row76_col6\" class=\"data row76 col6\" >Ipopt 3.14.16\\x3a Optimal Solution Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row77_col0\" class=\"data row77 col0\" >0</td>\n",
       "      <td id=\"T_7247e_row77_col1\" class=\"data row77 col1\" >0.069904</td>\n",
       "      <td id=\"T_7247e_row77_col2\" class=\"data row77 col2\" >127.001679</td>\n",
       "      <td id=\"T_7247e_row77_col3\" class=\"data row77 col3\" >0.066529</td>\n",
       "      <td id=\"T_7247e_row77_col4\" class=\"data row77 col4\" >127.138112</td>\n",
       "      <td id=\"T_7247e_row77_col5\" class=\"data row77 col5\" >40.034724</td>\n",
       "      <td id=\"T_7247e_row77_col6\" class=\"data row77 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row78_col0\" class=\"data row78 col0\" >68</td>\n",
       "      <td id=\"T_7247e_row78_col1\" class=\"data row78 col1\" >0.795017</td>\n",
       "      <td id=\"T_7247e_row78_col2\" class=\"data row78 col2\" >269.956568</td>\n",
       "      <td id=\"T_7247e_row78_col3\" class=\"data row78 col3\" >0.131875</td>\n",
       "      <td id=\"T_7247e_row78_col4\" class=\"data row78 col4\" >270.883459</td>\n",
       "      <td id=\"T_7247e_row78_col5\" class=\"data row78 col5\" >66.697377</td>\n",
       "      <td id=\"T_7247e_row78_col6\" class=\"data row78 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row79_col0\" class=\"data row79 col0\" >31</td>\n",
       "      <td id=\"T_7247e_row79_col1\" class=\"data row79 col1\" >0.770668</td>\n",
       "      <td id=\"T_7247e_row79_col2\" class=\"data row79 col2\" >277.050831</td>\n",
       "      <td id=\"T_7247e_row79_col3\" class=\"data row79 col3\" >0.953793</td>\n",
       "      <td id=\"T_7247e_row79_col4\" class=\"data row79 col4\" >278.775291</td>\n",
       "      <td id=\"T_7247e_row79_col5\" class=\"data row79 col5\" >66.872357</td>\n",
       "      <td id=\"T_7247e_row79_col6\" class=\"data row79 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row80_col0\" class=\"data row80 col0\" >40</td>\n",
       "      <td id=\"T_7247e_row80_col1\" class=\"data row80 col1\" >0.813956</td>\n",
       "      <td id=\"T_7247e_row80_col2\" class=\"data row80 col2\" >280.277295</td>\n",
       "      <td id=\"T_7247e_row80_col3\" class=\"data row80 col3\" >0.121654</td>\n",
       "      <td id=\"T_7247e_row80_col4\" class=\"data row80 col4\" >281.212905</td>\n",
       "      <td id=\"T_7247e_row80_col5\" class=\"data row80 col5\" >67.679053</td>\n",
       "      <td id=\"T_7247e_row80_col6\" class=\"data row80 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row81_col0\" class=\"data row81 col0\" >1</td>\n",
       "      <td id=\"T_7247e_row81_col1\" class=\"data row81 col1\" >0.344394</td>\n",
       "      <td id=\"T_7247e_row81_col2\" class=\"data row81 col2\" >305.453593</td>\n",
       "      <td id=\"T_7247e_row81_col3\" class=\"data row81 col3\" >0.063712</td>\n",
       "      <td id=\"T_7247e_row81_col4\" class=\"data row81 col4\" >305.861699</td>\n",
       "      <td id=\"T_7247e_row81_col5\" class=\"data row81 col5\" >68.299858</td>\n",
       "      <td id=\"T_7247e_row81_col6\" class=\"data row81 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row82_col0\" class=\"data row82 col0\" >57</td>\n",
       "      <td id=\"T_7247e_row82_col1\" class=\"data row82 col1\" >0.340179</td>\n",
       "      <td id=\"T_7247e_row82_col2\" class=\"data row82 col2\" >309.233336</td>\n",
       "      <td id=\"T_7247e_row82_col3\" class=\"data row82 col3\" >0.075331</td>\n",
       "      <td id=\"T_7247e_row82_col4\" class=\"data row82 col4\" >309.648846</td>\n",
       "      <td id=\"T_7247e_row82_col5\" class=\"data row82 col5\" >68.529457</td>\n",
       "      <td id=\"T_7247e_row82_col6\" class=\"data row82 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row83_col0\" class=\"data row83 col0\" >50</td>\n",
       "      <td id=\"T_7247e_row83_col1\" class=\"data row83 col1\" >0.728043</td>\n",
       "      <td id=\"T_7247e_row83_col2\" class=\"data row83 col2\" >312.977841</td>\n",
       "      <td id=\"T_7247e_row83_col3\" class=\"data row83 col3\" >0.108431</td>\n",
       "      <td id=\"T_7247e_row83_col4\" class=\"data row83 col4\" >313.814314</td>\n",
       "      <td id=\"T_7247e_row83_col5\" class=\"data row83 col5\" >67.712836</td>\n",
       "      <td id=\"T_7247e_row83_col6\" class=\"data row83 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row84_col0\" class=\"data row84 col0\" >44</td>\n",
       "      <td id=\"T_7247e_row84_col1\" class=\"data row84 col1\" >0.346279</td>\n",
       "      <td id=\"T_7247e_row84_col2\" class=\"data row84 col2\" >314.234411</td>\n",
       "      <td id=\"T_7247e_row84_col3\" class=\"data row84 col3\" >0.072887</td>\n",
       "      <td id=\"T_7247e_row84_col4\" class=\"data row84 col4\" >314.653576</td>\n",
       "      <td id=\"T_7247e_row84_col5\" class=\"data row84 col5\" >68.559452</td>\n",
       "      <td id=\"T_7247e_row84_col6\" class=\"data row84 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row85_col0\" class=\"data row85 col0\" >79</td>\n",
       "      <td id=\"T_7247e_row85_col1\" class=\"data row85 col1\" >0.870788</td>\n",
       "      <td id=\"T_7247e_row85_col2\" class=\"data row85 col2\" >361.172792</td>\n",
       "      <td id=\"T_7247e_row85_col3\" class=\"data row85 col3\" >0.154456</td>\n",
       "      <td id=\"T_7247e_row85_col4\" class=\"data row85 col4\" >362.198037</td>\n",
       "      <td id=\"T_7247e_row85_col5\" class=\"data row85 col5\" >66.938769</td>\n",
       "      <td id=\"T_7247e_row85_col6\" class=\"data row85 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row86_col0\" class=\"data row86 col0\" >98</td>\n",
       "      <td id=\"T_7247e_row86_col1\" class=\"data row86 col1\" >0.924783</td>\n",
       "      <td id=\"T_7247e_row86_col2\" class=\"data row86 col2\" >387.126736</td>\n",
       "      <td id=\"T_7247e_row86_col3\" class=\"data row86 col3\" >0.127787</td>\n",
       "      <td id=\"T_7247e_row86_col4\" class=\"data row86 col4\" >388.179306</td>\n",
       "      <td id=\"T_7247e_row86_col5\" class=\"data row86 col5\" >68.242717</td>\n",
       "      <td id=\"T_7247e_row86_col6\" class=\"data row86 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row87_col0\" class=\"data row87 col0\" >4</td>\n",
       "      <td id=\"T_7247e_row87_col1\" class=\"data row87 col1\" >0.482047</td>\n",
       "      <td id=\"T_7247e_row87_col2\" class=\"data row87 col2\" >394.756520</td>\n",
       "      <td id=\"T_7247e_row87_col3\" class=\"data row87 col3\" >0.099920</td>\n",
       "      <td id=\"T_7247e_row87_col4\" class=\"data row87 col4\" >395.338487</td>\n",
       "      <td id=\"T_7247e_row87_col5\" class=\"data row87 col5\" >68.432761</td>\n",
       "      <td id=\"T_7247e_row87_col6\" class=\"data row87 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row88_col0\" class=\"data row88 col0\" >85</td>\n",
       "      <td id=\"T_7247e_row88_col1\" class=\"data row88 col1\" >0.972169</td>\n",
       "      <td id=\"T_7247e_row88_col2\" class=\"data row88 col2\" >409.972165</td>\n",
       "      <td id=\"T_7247e_row88_col3\" class=\"data row88 col3\" >0.597257</td>\n",
       "      <td id=\"T_7247e_row88_col4\" class=\"data row88 col4\" >411.541592</td>\n",
       "      <td id=\"T_7247e_row88_col5\" class=\"data row88 col5\" >65.311769</td>\n",
       "      <td id=\"T_7247e_row88_col6\" class=\"data row88 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row89_col0\" class=\"data row89 col0\" >63</td>\n",
       "      <td id=\"T_7247e_row89_col1\" class=\"data row89 col1\" >1.312267</td>\n",
       "      <td id=\"T_7247e_row89_col2\" class=\"data row89 col2\" >431.966654</td>\n",
       "      <td id=\"T_7247e_row89_col3\" class=\"data row89 col3\" >0.726773</td>\n",
       "      <td id=\"T_7247e_row89_col4\" class=\"data row89 col4\" >434.005694</td>\n",
       "      <td id=\"T_7247e_row89_col5\" class=\"data row89 col5\" >85.165110</td>\n",
       "      <td id=\"T_7247e_row89_col6\" class=\"data row89 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row90_col0\" class=\"data row90 col0\" >74</td>\n",
       "      <td id=\"T_7247e_row90_col1\" class=\"data row90 col1\" >1.106714</td>\n",
       "      <td id=\"T_7247e_row90_col2\" class=\"data row90 col2\" >442.731974</td>\n",
       "      <td id=\"T_7247e_row90_col3\" class=\"data row90 col3\" >3.291488</td>\n",
       "      <td id=\"T_7247e_row90_col4\" class=\"data row90 col4\" >447.130177</td>\n",
       "      <td id=\"T_7247e_row90_col5\" class=\"data row90 col5\" >70.985508</td>\n",
       "      <td id=\"T_7247e_row90_col6\" class=\"data row90 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row91_col0\" class=\"data row91 col0\" >2</td>\n",
       "      <td id=\"T_7247e_row91_col1\" class=\"data row91 col1\" >1.116287</td>\n",
       "      <td id=\"T_7247e_row91_col2\" class=\"data row91 col2\" >500.957250</td>\n",
       "      <td id=\"T_7247e_row91_col3\" class=\"data row91 col3\" >0.199100</td>\n",
       "      <td id=\"T_7247e_row91_col4\" class=\"data row91 col4\" >502.272638</td>\n",
       "      <td id=\"T_7247e_row91_col5\" class=\"data row91 col5\" >65.075450</td>\n",
       "      <td id=\"T_7247e_row91_col6\" class=\"data row91 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row92_col0\" class=\"data row92 col0\" >93</td>\n",
       "      <td id=\"T_7247e_row92_col1\" class=\"data row92 col1\" >1.105509</td>\n",
       "      <td id=\"T_7247e_row92_col2\" class=\"data row92 col2\" >543.834011</td>\n",
       "      <td id=\"T_7247e_row92_col3\" class=\"data row92 col3\" >0.150730</td>\n",
       "      <td id=\"T_7247e_row92_col4\" class=\"data row92 col4\" >545.090249</td>\n",
       "      <td id=\"T_7247e_row92_col5\" class=\"data row92 col5\" >67.546800</td>\n",
       "      <td id=\"T_7247e_row92_col6\" class=\"data row92 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row93_col0\" class=\"data row93 col0\" >13</td>\n",
       "      <td id=\"T_7247e_row93_col1\" class=\"data row93 col1\" >1.126825</td>\n",
       "      <td id=\"T_7247e_row93_col2\" class=\"data row93 col2\" >558.540397</td>\n",
       "      <td id=\"T_7247e_row93_col3\" class=\"data row93 col3\" >0.198357</td>\n",
       "      <td id=\"T_7247e_row93_col4\" class=\"data row93 col4\" >559.865579</td>\n",
       "      <td id=\"T_7247e_row93_col5\" class=\"data row93 col5\" >65.860093</td>\n",
       "      <td id=\"T_7247e_row93_col6\" class=\"data row93 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row94_col0\" class=\"data row94 col0\" >29</td>\n",
       "      <td id=\"T_7247e_row94_col1\" class=\"data row94 col1\" >1.156050</td>\n",
       "      <td id=\"T_7247e_row94_col2\" class=\"data row94 col2\" >568.759235</td>\n",
       "      <td id=\"T_7247e_row94_col3\" class=\"data row94 col3\" >0.189839</td>\n",
       "      <td id=\"T_7247e_row94_col4\" class=\"data row94 col4\" >570.105124</td>\n",
       "      <td id=\"T_7247e_row94_col5\" class=\"data row94 col5\" >67.765885</td>\n",
       "      <td id=\"T_7247e_row94_col6\" class=\"data row94 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row95_col0\" class=\"data row95 col0\" >27</td>\n",
       "      <td id=\"T_7247e_row95_col1\" class=\"data row95 col1\" >1.186158</td>\n",
       "      <td id=\"T_7247e_row95_col2\" class=\"data row95 col2\" >587.985494</td>\n",
       "      <td id=\"T_7247e_row95_col3\" class=\"data row95 col3\" >0.183529</td>\n",
       "      <td id=\"T_7247e_row95_col4\" class=\"data row95 col4\" >589.355181</td>\n",
       "      <td id=\"T_7247e_row95_col5\" class=\"data row95 col5\" >66.753797</td>\n",
       "      <td id=\"T_7247e_row95_col6\" class=\"data row95 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row96_col0\" class=\"data row96 col0\" >77</td>\n",
       "      <td id=\"T_7247e_row96_col1\" class=\"data row96 col1\" >1.140915</td>\n",
       "      <td id=\"T_7247e_row96_col2\" class=\"data row96 col2\" >613.003252</td>\n",
       "      <td id=\"T_7247e_row96_col3\" class=\"data row96 col3\" >0.176805</td>\n",
       "      <td id=\"T_7247e_row96_col4\" class=\"data row96 col4\" >614.320972</td>\n",
       "      <td id=\"T_7247e_row96_col5\" class=\"data row96 col5\" >67.783945</td>\n",
       "      <td id=\"T_7247e_row96_col6\" class=\"data row96 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row97_col0\" class=\"data row97 col0\" >83</td>\n",
       "      <td id=\"T_7247e_row97_col1\" class=\"data row97 col1\" >1.272586</td>\n",
       "      <td id=\"T_7247e_row97_col2\" class=\"data row97 col2\" >696.656276</td>\n",
       "      <td id=\"T_7247e_row97_col3\" class=\"data row97 col3\" >0.177005</td>\n",
       "      <td id=\"T_7247e_row97_col4\" class=\"data row97 col4\" >698.105867</td>\n",
       "      <td id=\"T_7247e_row97_col5\" class=\"data row97 col5\" >68.163689</td>\n",
       "      <td id=\"T_7247e_row97_col6\" class=\"data row97 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row98_col0\" class=\"data row98 col0\" >11</td>\n",
       "      <td id=\"T_7247e_row98_col1\" class=\"data row98 col1\" >1.232298</td>\n",
       "      <td id=\"T_7247e_row98_col2\" class=\"data row98 col2\" >709.665890</td>\n",
       "      <td id=\"T_7247e_row98_col3\" class=\"data row98 col3\" >0.167944</td>\n",
       "      <td id=\"T_7247e_row98_col4\" class=\"data row98 col4\" >711.066132</td>\n",
       "      <td id=\"T_7247e_row98_col5\" class=\"data row98 col5\" >68.146790</td>\n",
       "      <td id=\"T_7247e_row98_col6\" class=\"data row98 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7247e_row99_col0\" class=\"data row99 col0\" >36</td>\n",
       "      <td id=\"T_7247e_row99_col1\" class=\"data row99 col1\" >3.999853</td>\n",
       "      <td id=\"T_7247e_row99_col2\" class=\"data row99 col2\" >1325.132178</td>\n",
       "      <td id=\"T_7247e_row99_col3\" class=\"data row99 col3\" >44.219507</td>\n",
       "      <td id=\"T_7247e_row99_col4\" class=\"data row99 col4\" >1373.351539</td>\n",
       "      <td id=\"T_7247e_row99_col5\" class=\"data row99 col5\" >67.302621</td>\n",
       "      <td id=\"T_7247e_row99_col6\" class=\"data row99 col6\" >Ipopt 3.14.16\\x3a Converged to a locally infeasible point. Problem may be infeasible.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f362e639850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_data = res_plotter(results_dict, multi_result=True, met_meta=met_meta, rxn_meta=rxn_meta)\n",
    "display(res_data.summarize_runs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f65898b-0104-45b7-8a68-3f2f07021c9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- Status: ok\n",
      "  Message: Ipopt 3.14.16\\x3a Optimal Solution Found\n",
      "  Termination condition: optimal\n",
      "  Id: 0\n",
      "  Error rc: 0\n",
      "  Time: 0.4469280242919922\n",
      "\n",
      "Results class (name=None) with 100 experiments (Active=58):\n",
      "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99\n"
     ]
    }
   ],
   "source": [
    "res_data.sel_result(58)\n",
    "print(res_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "595f0e10-8205-445a-bfa3-1a499d026bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n_results = res_data.choose_top_n('obj', 10, ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9cc2da-ea2e-4f96-aeca-271a6a6e1fa9",
   "metadata": {},
   "source": [
    "#### Secreted Fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d88e344-d9a5-420b-bb89-64dd090ca669",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'markersize': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1290 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_secretedflux(time_col= 'Time (WD)', meas_cols=None, ncols=5, figsize=None, res_ids=top_n_results[0:2], smooth_kwargs={'markersize': 0})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca78ec5-d8ab-44e7-b2d2-8c593f727178",
   "metadata": {},
   "source": [
    "#### Alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1b37e04-0243-41b5-86a5-608452928ed7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x430 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_alphas(time_col='Time (WD)', meas_cols=None, ncols=5, res_ids=top_n_results, figsize=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f0cb38-1827-4332-95bf-8e3cd0856a30",
   "metadata": {},
   "source": [
    "#### Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c8267b9-c25a-4f99-a733-bda4072f0979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x860 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_betas(time_col = 'Time (WD)', meas_cols=None, ncols=5, figsize=None, res_ids=top_n_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74dc2ca-c88e-46bd-8296-ae7b58e09430",
   "metadata": {},
   "source": [
    "#### Gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "29c55e42-d053-432f-b2e9-e7d825832f25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x430 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_gamma(time_col = 'Time (WD)', meas_cols=None, ncols=5, figsize=None, res_ids=top_n_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a571d691-7165-4764-bb3f-bd2ce4ee0f30",
   "metadata": {},
   "source": [
    "#### Entry Flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2edd31e-4b53-4d0e-aaa5-a5e32d3cb5b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.YTick at 0x7f362d6aedd0>,\n",
       " <matplotlib.axis.YTick at 0x7f362d799f10>,\n",
       " <matplotlib.axis.YTick at 0x7f362db30510>,\n",
       " <matplotlib.axis.YTick at 0x7f362d42bf50>,\n",
       " <matplotlib.axis.YTick at 0x7f362d420550>,\n",
       " <matplotlib.axis.YTick at 0x7f362d6bca50>,\n",
       " <matplotlib.axis.YTick at 0x7f362d6dd8d0>,\n",
       " <matplotlib.axis.YTick at 0x7f362d435a10>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x430 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_entry_flux(time_col='Time (WD)', meas_cols=None, ncols=5, figsize=None, res_ids=top_n_results)\n",
    "sns.lineplot(init_model.spec_prod, x='Time (WD)', y='Spec Prod (pg/cells/day)', c='g', linestyle='-.', ax=fig.axes[0])\n",
    "tick_mins = np.floor(init_model.spec_prod['Spec Prod (pg/cells/day)'].min()) - 5\n",
    "tick_maxs = np.ceil(init_model.spec_prod['Spec Prod (pg/cells/day)'].max()) + 5\n",
    "fig.axes[0].set_yticks(np.arange(tick_mins, tick_maxs, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7bf3100-79e1-4c6b-b657-c66c7e28c32c",
   "metadata": {},
   "source": [
    "#### Reference Fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "864f6948-0227-411d-83b2-63b44d614d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None,\n",
       " None]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_vref(x_col='Reaction ID', meas_cols=None, ncols=5, figsize=(5, 5), res_ids=top_n_results)\n",
    "plt.setp(fig.axes[0].get_xticklabels(), fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e442d9-97d9-4924-acd4-fd60077986cf",
   "metadata": {},
   "source": [
    "#### Internal Fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a0c4088-f394-44ab-bfef-2d3af64c249b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x860 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res_data.plot_internalflux(time_col='Time (WD)', meas_cols=None, ncols=5, figsize=None, res_ids=top_n_results)"
   ]
  }
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