Results March 2019.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparisons between different versions of skprocrustes solvers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skprocrustes as skp\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Comparison between all solvers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gkbsolver = skp.GKBSolver(verbose=0)\n",
    "spgsolver = skp.SPGSolver(verbose=0)\n",
    "gpisolver = skp.GPISolver(verbose=0)\n",
    "ebsolver = skp.EBSolver(verbose=0)\n",
    "gbbsolver = skp.GBBSolver(verbose=0)\n",
    "gkbgbbsolver = skp.GKBSolver(verbose=0, inner_solver='gbb')\n",
    "gkbblobopsolver = skp.GKBSolver(verbose=0, bloboptest = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem1 = skp.ProcrustesProblem((500,500,10,10), problemnumber=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1: All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results1_gkb = gkbsolver.solve(problem1); t1_gkb = time.time()-t0\n",
    "t0 = time.time(); results1_spg = spgsolver.solve(problem1); t1_spg = time.time()-t0\n",
    "t0 = time.time(); results1_gpi = gpisolver.solve(problem1); t1_gpi = time.time()-t0\n",
    "t0 = time.time(); results1_eb = ebsolver.solve(problem1); t1_eb = time.time()-t0\n",
    "t0 = time.time(); results1_gbb = gbbsolver.solve(problem1); t1_gbb = time.time()-t0\n",
    "t0 = time.time(); results1_gkbgbb = gkbgbbsolver.solve(problem1); t1_gkbgbb = time.time()-t0\n",
    "t0 = time.time(); results1_gkbblobop = gkbblobopsolver.solve(problem1); t1_gkbblobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1: Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, eb, gbb, gkbgbb, gkbblobop = plt.bar([0,1,2,3,4,5,6], [t1_gkb, t1_spg, t1_gpi, t1_eb, t1_gbb, t1_gkbgbb, t1_gkbblobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "gbb.set_facecolor('y')\n",
    "gkbgbb.set_facecolor('k')\n",
    "gkbblobop.set_facecolor('c')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5,6])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'GBB', 'GKB+GBB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, gbb, gkbgbb, gkbblobop = plt.bar([0,1,2,3,4,5], [t1_gkb, t1_spg, t1_gpi, t1_gbb, t1_gkbgbb, t1_gkbblobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "gbb.set_facecolor('y')\n",
    "gkbgbb.set_facecolor('k')\n",
    "gkbblobop.set_facecolor('c')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'GBB', 'GKB+GBB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GKB</th>\n",
       "      <td>0.111337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPG</th>\n",
       "      <td>0.012094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPI</th>\n",
       "      <td>0.043562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EB</th>\n",
       "      <td>0.968036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBB</th>\n",
       "      <td>0.023611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+GBB</th>\n",
       "      <td>0.076657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+BLOBOP</th>\n",
       "      <td>0.065112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            problem1\n",
       "GKB         0.111337\n",
       "SPG         0.012094\n",
       "GPI         0.043562\n",
       "EB          0.968036\n",
       "GBB         0.023611\n",
       "GKB+GBB     0.076657\n",
       "GKB+BLOBOP  0.065112"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({'GKB' : [t1_gkb], \n",
    "                        'SPG' : [t1_spg], \n",
    "                        'GPI' : [t1_gpi], \n",
    "                        'EB' : [t1_eb], \n",
    "                        'GBB' : [t1_gbb], \n",
    "                        'GKB+GBB' : [t1_gkbgbb],\n",
    "                        'GKB+BLOBOP': [t1_gkbblobop]}, index=['problem1'])\n",
    "results = results.T\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem2 = skp.ProcrustesProblem((100,100,5,5), problemnumber=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2: All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results2_gkb = gkbsolver.solve(problem2); t2_gkb = time.time()-t0\n",
    "t0 = time.time(); results2_spg = spgsolver.solve(problem2); t2_spg = time.time()-t0\n",
    "t0 = time.time(); results2_gpi = gpisolver.solve(problem2); t2_gpi = time.time()-t0\n",
    "t0 = time.time(); results2_eb = ebsolver.solve(problem2); t2_eb = time.time()-t0\n",
    "t0 = time.time(); results2_gbb = gbbsolver.solve(problem2); t2_gbb = time.time()-t0\n",
    "t0 = time.time(); results2_gkbgbb = gkbgbbsolver.solve(problem2); t2_gkbgbb = time.time()-t0\n",
    "t0 = time.time(); results2_gkbblobop = gkbblobopsolver.solve(problem2); t2_gkbblobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 2')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, eb, gbb = plt.bar([0,1,2,3,4], [t2_gkb, t2_spg, t2_gpi, t2_eb, t2_gbb])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "gbb.set_facecolor('y')\n",
    "#gkbgbb.set_facecolor('k')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'GBB'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 2')\n",
    "\n",
    "gkb, spg, gpi, eb, gbb, gkbgbb, gkbblobop = plt.bar([0,1,2,3,4,5,6], [t2_gkb, t2_spg, t2_gpi, t2_eb, t2_gbb, t2_gkbgbb, t2_gkbblobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "gbb.set_facecolor('y')\n",
    "gkbgbb.set_facecolor('k')\n",
    "gkbblobop.set_facecolor('c')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5,6])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'GBB', 'GKB+GBB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem1</th>\n",
       "      <th>problem2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GKB</th>\n",
       "      <td>0.111337</td>\n",
       "      <td>1.732536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPG</th>\n",
       "      <td>0.012094</td>\n",
       "      <td>0.394459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPI</th>\n",
       "      <td>0.043562</td>\n",
       "      <td>0.355606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EB</th>\n",
       "      <td>0.968036</td>\n",
       "      <td>12.921742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBB</th>\n",
       "      <td>0.023611</td>\n",
       "      <td>4.137173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+GBB</th>\n",
       "      <td>0.076657</td>\n",
       "      <td>6.378740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+BLOBOP</th>\n",
       "      <td>0.065112</td>\n",
       "      <td>1.633111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            problem1   problem2\n",
       "GKB         0.111337   1.732536\n",
       "SPG         0.012094   0.394459\n",
       "GPI         0.043562   0.355606\n",
       "EB          0.968036  12.921742\n",
       "GBB         0.023611   4.137173\n",
       "GKB+GBB     0.076657   6.378740\n",
       "GKB+BLOBOP  0.065112   1.633111"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['problem2'] = pd.Series([t2_gkb, t2_spg, t2_gpi, t2_eb, t2_gbb, t2_gkbgbb, t2_gkbblobop], index=results.index)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem3 = skp.ProcrustesProblem((500, 500, 5, 5), problemnumber=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: GKB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 5.131667711540999e-06\n",
      "normcrit : 0.01751056895271069\n",
      "error : 0.03580606405564048\n",
      "cpu : 5.796164035797119\n",
      "nbiter : 1692.62\n",
      "nfev : 1950.970000000002\n",
      "blocksteps : 100\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time(); results3_gkb = gkbsolver.solve(problem3); t3_gkb = time.time()-t0\n",
    "results3_gkb.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: SPG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04762768745422363\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time(); results3_spg = spgsolver.solve(problem3); t3_spg = time.time()-t0; print(t3_spg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 5.132780151942836e-06\n",
      "normcrit : 0.0003815177725226038\n",
      "error : 0.035804441667312524\n",
      "cpu : 0.047443389892578125\n",
      "nbiter : 34\n",
      "nfev : 35.0\n"
     ]
    }
   ],
   "source": [
    "results3_spg.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: GPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3965134620666504\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time(); results3_gpi = gpisolver.solve(problem3); t3_gpi = time.time()-t0; print(t3_gpi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 1.551334913511932e-05\n",
      "normcrit : 9.974001297310054e-07\n",
      "error : 0.03564933204935722\n",
      "cpu : 0.39629149436950684\n",
      "nbiter : 86\n",
      "nfev : 87\n"
     ]
    }
   ],
   "source": [
    "results3_gpi.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: EB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.9253568649292\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time(); results3_eb = ebsolver.solve(problem3); t3_eb = time.time()-t0; print(t3_eb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 5.131185645047931e-06\n",
      "error : 0.03580563112422961\n",
      "cpu : 18.92514681816101\n",
      "nbiter : 220\n",
      "nfev : 221\n"
     ]
    }
   ],
   "source": [
    "results3_eb.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.637048721313477\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time(); results3_gbb = gbbsolver.solve(problem3); t3_gbb = time.time()-t0; print(t3_gbb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 6.132167199890109e-06\n",
      "error : 0.055769753038724015\n",
      "cpu : 5.6368420124053955\n",
      "nbiter : 3020\n",
      "nfev : 10834\n"
     ]
    }
   ],
   "source": [
    "results3_gbb.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3: All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 3')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, eb, gbb = plt.bar([0,1,2,3,4], [t3_gkb, t3_spg, t3_gpi, t3_eb, t3_gbb])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "gbb.set_facecolor('y')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'GBB'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem1</th>\n",
       "      <th>problem2</th>\n",
       "      <th>problem3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GKB</th>\n",
       "      <td>0.069958</td>\n",
       "      <td>4.170301</td>\n",
       "      <td>5.796366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPG</th>\n",
       "      <td>0.030239</td>\n",
       "      <td>1.733156</td>\n",
       "      <td>0.047628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPI</th>\n",
       "      <td>0.057133</td>\n",
       "      <td>0.681897</td>\n",
       "      <td>0.396513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EB</th>\n",
       "      <td>0.905570</td>\n",
       "      <td>13.386660</td>\n",
       "      <td>18.925357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBB</th>\n",
       "      <td>0.059863</td>\n",
       "      <td>5.319885</td>\n",
       "      <td>5.637049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+GBB</th>\n",
       "      <td>0.071246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         problem1   problem2   problem3\n",
       "GKB      0.069958   4.170301   5.796366\n",
       "SPG      0.030239   1.733156   0.047628\n",
       "GPI      0.057133   0.681897   0.396513\n",
       "EB       0.905570  13.386660  18.925357\n",
       "GBB      0.059863   5.319885   5.637049\n",
       "GKB+GBB  0.071246   0.000000   0.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['problem3'] = pd.Series([t3_gkb, t3_spg, t3_gpi, t3_eb, t3_gbb, 0], index=results.index)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Comparison between blobop and old residual for GKBSolver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gkbsolver_blobop = skp.GKBSolver(verbose=0, bloboptest = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results1_gkbblobop = gkbsolver_blobop.solve(problem1); t1_blobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 2.608082314765709e-10\n",
      "normcrit : 0.0001910561299554899\n",
      "error : 1.1002118566061557e-06\n",
      "cpu : 0.06883001327514648\n",
      "nbiter : 1.1600000000000001\n",
      "nfev : 2.9600000000000013\n",
      "blocksteps : 5\n"
     ]
    }
   ],
   "source": [
    "results1_gkbblobop.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 2.578793622029034e-10\n",
      "normcrit : 0.00018998120729215638\n",
      "error : 1.1233282908519718e-06\n",
      "cpu : 0.06975817680358887\n",
      "nbiter : 1.1600000000000001\n",
      "nfev : 2.9600000000000013\n",
      "blocksteps : 5\n"
     ]
    }
   ],
   "source": [
    "results1_gkb.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, blobop = plt.bar([0,1], [t1_gkb, t1_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "blobop.set_facecolor('y')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1])\n",
    "ax.set_xticklabels(['GKB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, eb, gbb, blobop = plt.bar([0,1,2,3,4,5], [t1_gkb, t1_spg, t1_gpi, t1_eb, t1_gbb, t1_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "gbb.set_facecolor('y')\n",
    "blobop.set_facecolor('k')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'GBB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, gbb, blobop = plt.bar([0,1,2,3,4], [t1_gkb, t1_spg, t1_gpi, t1_gbb, t1_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "#eb.set_facecolor('m')\n",
    "gbb.set_facecolor('y')\n",
    "blobop.set_facecolor('k')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'GBB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results2_gkbblobop = gkbsolver_blobop.solve(problem2); t2_blobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 2.925400923378415e-07\n",
      "normcrit : 0.022602674945456508\n",
      "error : 0.002254040065187797\n",
      "cpu : 2.9044535160064697\n",
      "nbiter : 4153.35\n",
      "nfev : 5753.200000000004\n",
      "blocksteps : 20\n"
     ]
    }
   ],
   "source": [
    "results2_gkbblobop.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 2.1445174383833714e-07\n",
      "normcrit : 0.018800891291422688\n",
      "error : 0.0019578669028790818\n",
      "cpu : 4.170146703720093\n",
      "nbiter : 5451.1\n",
      "nfev : 7778.850000000015\n",
      "blocksteps : 20\n"
     ]
    }
   ],
   "source": [
    "results2_gkb.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 2')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, blobop = plt.bar([0,1], [t2_gkb, t2_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "blobop.set_facecolor('y')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1])\n",
    "ax.set_xticklabels(['GKB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 2')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, eb, blobop = plt.bar([0,1,2,3,4], [t2_gkb, t2_spg, t2_gpi, t2_eb, t2_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "blobop.set_facecolor('y')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results3_gkbblobop = gkbsolver_blobop.solve(problem3); t3_blobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 5.131667711526123e-06\n",
      "normcrit : 0.017510568952651948\n",
      "error : 0.03580606405564164\n",
      "cpu : 5.470430135726929\n",
      "nbiter : 1692.83\n",
      "nfev : 1951.180000000002\n",
      "blocksteps : 100\n"
     ]
    }
   ],
   "source": [
    "results3_gkbblobop.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 5.131667711540999e-06\n",
      "normcrit : 0.01751056895271069\n",
      "error : 0.03580606405564048\n",
      "cpu : 5.796164035797119\n",
      "nbiter : 1692.62\n",
      "nfev : 1950.970000000002\n",
      "blocksteps : 100\n"
     ]
    }
   ],
   "source": [
    "results3_gkb.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 3')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, blobop = plt.bar([0,1], [t3_gkb, t3_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "blobop.set_facecolor('y')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1])\n",
    "ax.set_xticklabels(['GKB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 3')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, spg, gpi, eb, blobop = plt.bar([0,1,2,3,4], [t3_gkb, t3_spg, t3_gpi, t3_eb, t3_blobop])\n",
    "gkb.set_facecolor('r')\n",
    "spg.set_facecolor('g')\n",
    "gpi.set_facecolor('b')\n",
    "eb.set_facecolor('m')\n",
    "blobop.set_facecolor('y')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4])\n",
    "ax.set_xticklabels(['GKB', 'SPG', 'GPI', 'EB', 'BLOBOP'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem1</th>\n",
       "      <th>problem2</th>\n",
       "      <th>problem3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GKB</th>\n",
       "      <td>0.069958</td>\n",
       "      <td>4.170301</td>\n",
       "      <td>5.796366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPG</th>\n",
       "      <td>0.030239</td>\n",
       "      <td>1.733156</td>\n",
       "      <td>0.047628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPI</th>\n",
       "      <td>0.057133</td>\n",
       "      <td>0.681897</td>\n",
       "      <td>0.396513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EB</th>\n",
       "      <td>0.905570</td>\n",
       "      <td>13.386660</td>\n",
       "      <td>18.925357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBB</th>\n",
       "      <td>0.059863</td>\n",
       "      <td>5.319885</td>\n",
       "      <td>5.637049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+GBB</th>\n",
       "      <td>0.071246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOBOP</th>\n",
       "      <td>0.069043</td>\n",
       "      <td>2.904620</td>\n",
       "      <td>5.470636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         problem1   problem2   problem3\n",
       "GKB      0.069958   4.170301   5.796366\n",
       "SPG      0.030239   1.733156   0.047628\n",
       "GPI      0.057133   0.681897   0.396513\n",
       "EB       0.905570  13.386660  18.925357\n",
       "GBB      0.059863   5.319885   5.637049\n",
       "GKB+GBB  0.071246   0.000000   0.000000\n",
       "BLOBOP   0.069043   2.904620   5.470636"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = pd.DataFrame({'problem1' : t1_blobop, 'problem2' : t2_blobop, 'problem3' : t3_blobop}, index=['BLOBOP'])\n",
    "results = results.append(r)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Comparison between svd and polar decomposition on GKBSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "gkbpolar = skp.GKBSolver(verbose=0, polar=\"ns\")\n",
    "gkbpolarblobop = skp.GKBSolver(verbose=0, polar=\"ns\", bloboptest=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results1_gkbpolar = gkbpolar.solve(problem1); t1_gkbpolar = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 2.497367832737372e-10\n",
      "normcrit : 0.00018753618499880168\n",
      "error : 1.165856725168374e-06\n",
      "cpu : 0.09996747970581055\n",
      "nbiter : 1.1600000000000001\n",
      "nfev : 2.9600000000000013\n",
      "blocksteps : 5\n"
     ]
    }
   ],
   "source": [
    "results1_gkbpolar.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results1_gkbpolarblobop = gkbpolarblobop.solve(problem1); t1_gkbpolarblobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : Optimization terminated successfully.\n",
      "fun : 2.5171422703106026e-10\n",
      "normcrit : 0.00018855805904438035\n",
      "error : 1.1367281198579056e-06\n",
      "cpu : 0.06825661659240723\n",
      "nbiter : 1.1600000000000001\n",
      "nfev : 2.9600000000000013\n",
      "blocksteps : 5\n"
     ]
    }
   ],
   "source": [
    "results1_gkbpolarblobop.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, polarblobop, polar = plt.bar([0,1,2], [t1_gkb, t1_gkbpolarblobop, t1_gkbpolar])\n",
    "gkb.set_facecolor('r')\n",
    "polarblobop.set_facecolor('g')\n",
    "polar.set_facecolor('b')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2])\n",
    "ax.set_xticklabels(['GKB', 'POLAR+BLOBOP', 'POLAR'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results2_gkbpolar = gkbpolar.solve(problem2); t2_gkbpolar = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 4.820270216084519e-08\n",
      "normcrit : 0.007214606235196402\n",
      "error : 0.0011032592914477493\n",
      "cpu : 3.780449867248535\n",
      "nbiter : 3708.5\n",
      "nfev : 5103.600000000003\n",
      "blocksteps : 20\n"
     ]
    }
   ],
   "source": [
    "results2_gkbpolar.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results2_gkbpolarblobop = gkbpolarblobop.solve(problem2); t2_gkbpolarblobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 2.311772683931615e-07\n",
      "normcrit : 0.019707582380820297\n",
      "error : 0.0020224139550518733\n",
      "cpu : 5.417364835739136\n",
      "nbiter : 5371.1\n",
      "nfev : 7593.850000000003\n",
      "blocksteps : 20\n"
     ]
    }
   ],
   "source": [
    "results2_gkbpolarblobop.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 2')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, polarblobop, polar = plt.bar([0,1,2], [t2_gkb, t2_gkbpolarblobop, t2_gkbpolar])\n",
    "gkb.set_facecolor('r')\n",
    "polarblobop.set_facecolor('g')\n",
    "polar.set_facecolor('b')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2])\n",
    "ax.set_xticklabels(['GKB', 'POLAR+BLOBOP', 'POLAR'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results3_gkbpolar = gkbpolar.solve(problem3); t3_gkbpolar = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 5.1316677115433824e-06\n",
      "normcrit : 0.01751056895291697\n",
      "error : 0.035806064055638895\n",
      "cpu : 7.435473918914795\n",
      "nbiter : 1692.86\n",
      "nfev : 1951.210000000002\n",
      "blocksteps : 100\n"
     ]
    }
   ],
   "source": [
    "results3_gkbpolar.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = time.time(); results3_gkbpolarblobop = gkbpolarblobop.solve(problem3); t3_gkbpolarblobop = time.time()-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========\n",
      " Summary:\n",
      "=========\n",
      "success : True\n",
      "status : 0\n",
      "message : No further progress can be made.\n",
      "fun : 5.131667711539431e-06\n",
      "normcrit : 0.017510568952969263\n",
      "error : 0.03580606405564402\n",
      "cpu : 6.525835275650024\n",
      "nbiter : 1692.77\n",
      "nfev : 1951.1200000000022\n",
      "blocksteps : 100\n"
     ]
    }
   ],
   "source": [
    "results3_gkbpolarblobop.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 3')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, polarblobop, polar = plt.bar([0,1,2], [t3_gkb, t3_gkbpolarblobop, t3_gkbpolar])\n",
    "gkb.set_facecolor('r')\n",
    "polarblobop.set_facecolor('g')\n",
    "polar.set_facecolor('b')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2])\n",
    "ax.set_xticklabels(['GKB', 'POLAR+BLOBOP', 'POLAR'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem1</th>\n",
       "      <th>problem2</th>\n",
       "      <th>problem3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GKB</th>\n",
       "      <td>0.069958</td>\n",
       "      <td>4.170301</td>\n",
       "      <td>5.796366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPG</th>\n",
       "      <td>0.030239</td>\n",
       "      <td>1.733156</td>\n",
       "      <td>0.047628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPI</th>\n",
       "      <td>0.057133</td>\n",
       "      <td>0.681897</td>\n",
       "      <td>0.396513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EB</th>\n",
       "      <td>0.905570</td>\n",
       "      <td>13.386660</td>\n",
       "      <td>18.925357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBB</th>\n",
       "      <td>0.059863</td>\n",
       "      <td>5.319885</td>\n",
       "      <td>5.637049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GKB+GBB</th>\n",
       "      <td>0.071246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BLOBOP</th>\n",
       "      <td>0.069043</td>\n",
       "      <td>2.904620</td>\n",
       "      <td>5.470636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLAR</th>\n",
       "      <td>0.100189</td>\n",
       "      <td>3.780615</td>\n",
       "      <td>7.435705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>POLAR+BLOBOP</th>\n",
       "      <td>0.068440</td>\n",
       "      <td>5.417522</td>\n",
       "      <td>6.526041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              problem1   problem2   problem3\n",
       "GKB           0.069958   4.170301   5.796366\n",
       "SPG           0.030239   1.733156   0.047628\n",
       "GPI           0.057133   0.681897   0.396513\n",
       "EB            0.905570  13.386660  18.925357\n",
       "GBB           0.059863   5.319885   5.637049\n",
       "GKB+GBB       0.071246   0.000000   0.000000\n",
       "BLOBOP        0.069043   2.904620   5.470636\n",
       "POLAR         0.100189   3.780615   7.435705\n",
       "POLAR+BLOBOP  0.068440   5.417522   6.526041"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = pd.DataFrame({'problem1' : t1_gkbpolar, 'problem2' : t2_gkbpolar, 'problem3' : t3_gkbpolar}, index=['POLAR'])\n",
    "results = results.append(r)\n",
    "r = pd.DataFrame({'problem1' : t1_gkbpolarblobop, 'problem2' : t2_gkbpolarblobop, 'problem3' : t3_gkbpolarblobop}, index=['POLAR+BLOBOP'])\n",
    "results = results.append(r)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Comparison between all versions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 1')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, blobop, polarblobop, polar, spg, gpi, eb = plt.bar([0, 1, 2, 3, 4, 5, 6], [t1_gkb, t1_blobop, t1_gkbpolarblobop, t1_gkbpolar, t1_spg, t1_gpi, t1_eb])\n",
    "gkb.set_facecolor('r')\n",
    "blobop.set_facecolor('k')\n",
    "polarblobop.set_facecolor('g')\n",
    "polar.set_facecolor('b')\n",
    "spg.set_facecolor('y')\n",
    "gpi.set_facecolor('m')\n",
    "eb.set_facecolor('c')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5,6])\n",
    "ax.set_xticklabels(['GKB', 'BLOBOP', 'POLAR+BLOBOP', 'POLAR', 'SPG', 'GPI', 'EB'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 2')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEICAYAAABS0fM3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAFyRJREFUeJzt3XuYZHV95/H3R0AHEFdH2ivgYHRxo0GENhg1ys0EExU07gaiCRLW0d2o0ch6CRrImhg3uNHEa8Yb7iOiBsUQ4wUiM4hKwB4cuQhyURDQQKNGucn1u3+c07EoumtquruqZua8X8/TT1edc+r8vnWq6nzq/M6lUlVIkrrrPpMuQJI0WQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgzSPJcUk+NmD8lUkOGmdN0qgYBNpqtCvnW5PclOS6JB9Jcv9J17UUSY5Isj7Jz5Jck+Svk2w76bq0dTEItLV5blXdH9gbeDLwpv4J0thS3vs7AK8Gdgb2BQ4Ejp5oRdrqbCkfBmmTVNW1wBeAJwAkWZfkL5N8DbgFeHSSRyQ5NcmPk1ye5KV9s1mR5JNJbkxyXpInztdWkvskeUOSK5L8KMmnkqxsx61KUkmOTHJ1kp8keXmSJyc5P8m/J3n3gOfxvqo6q6pub5/TicDTlmERSf/BINBWKcmuwG8B3+wZ/PvAamAn4CrgJOAa4BHAC4G3JjmwZ/pDgH8AVgIfBz6bZLt5mnsVcCjwzHZePwHe0zfNvsBjgd8F3gkcAxwEPB74b0meOeRTewZw0ZDTSkMxCLS1+WySfwe+CpwJvLVn3AlVdVFV3Qk8DHg68Pqq+nlVbQA+SBMWc9ZX1clVdQfwN8AK4CnztPky4JiquqaqbgOOA17Y15f/lrad04CbgZOq6vr2W/5ZwJM29sSSHAlMA28fYjlIQ3Onk7Y2h1bVvyww7uqe248AflxVN/YMu4pmRXuv6avq7iRzWw/9HgWckuTunmF3AQ/tuX9dz+1b57k/cKd2kkOBtwEHVdUNg6aVNpVbBOqS3kvt/gBYmWSnnmG7Adf23N917ka7c3mX9nH9rgaeXVUP7Plb0X7bX7IkBwMfoNkRfsFyzFPqZRCok6rqauDrwF8lWZFkT+Aomp2xc/ZJ8oK2i+fVwG3Av84zu/cDf5nkUQBJppIcshx1Jjmgrel3qurc5Zin1M8gUJcdDqyi+ZZ/CnBsVZ3eM/4faXbu/oRm38EL2v0F/f4WOBU4LcmNNGGx7zLV+GbgPwGfb8+PuCnJF5Zp3hIA8YdpJKnb3CKQpI4zCCSp40YWBEk+nOT6JBfOM+7o9mzLnUfVviRpOKPcIjgBOLh/YHvG57OA74+wbUnSkEZ2QllVfSXJqnlGvQN4Hc0RGUPZeeeda9Wq+WYlSVrI+vXrb6iqqY1NN9Yzi5M8D7i2qr6VZGPTrqa5Lgy77bYbMzMzY6hQkrYeSa4aZrqx7SxOsgPNhbb+bJjpq2pNVU1X1fTU1EYDTZK0SOM8auiXgN2BbyW5kuZ0/fOSPGyMNUiS+oyta6i9RspD5u63YTDtBbQkabJGefjoScDZwB7tT+wdNaq2JEmLN8qjhg7fyPhVo2pbkjQ8zyyWpI4zCCSp4wwCSeo4g0CSOs7fLJakHlm3btIl3EPtt9/I23CLQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOm5kQZDkw0muT3Jhz7Djk1yS5PwkpyR54KjalyQNZ5RbBCcAB/cNOx14QlXtCVwKvHGE7UuShjCyIKiqrwA/7ht2WlXd2d79V2CXUbUvSRrOJPcR/CHwhYVGJlmdZCbJzOzs7BjLkqRumUgQJDkGuBM4caFpqmpNVU1X1fTU1NT4ipOkjtl23A0mOQJ4DnBgVdW425ck3dNYgyDJwcDrgWdW1S3jbFuSNL9RHj56EnA2sEeSa5IcBbwb2Ak4PcmGJO8fVfuSpOGMbIugqg6fZ/CHRtWeJGlxPLNYkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOG1kQJPlwkuuTXNgzbGWS05Nc1v5/0KjalyQNZ5RbBCcAB/cNewPw5ap6LPDl9r4kaYJGFgRV9RXgx32DDwE+2t7+KHDoqNqXJA1n3PsIHlpVPwRo/z9koQmTrE4yk2RmdnZ2bAVKUtdstjuLq2pNVU1X1fTU1NSky5Gkrda4g+C6JA8HaP9fP+b2JUl9xh0EpwJHtLePAP5xzO1LkvqM8vDRk4CzgT2SXJPkKOBtwLOSXAY8q70vSZqgbUc146o6fIFRB46qTUnSpttsdxZLksbDIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4bKgiSPD3Jke3tqSS7j7YsSdK4bDQIkhwLvB54YztoO+BjoyxKkjQ+w2wRPB94HnAzQFX9ANhplEVJksZnmCC4vaoKKIAkO462JEnSOA0TBJ9K8vfAA5O8FPgX4ANLaTTJa5JclOTCJCclWbGU+UmSFm+jQVBVbwdOBj4N7AH8WVW9a7ENJnkk8CpguqqeAGwDHLbY+UmSlmbbYSaqqtOTnDM3fZKVVfXjJba7fZI7gB2AHyxhXpKkJRjmqKGXJbkOOB+YAda3/xelqq4F3g58H/gh8NOqOm2edlcnmUkyMzs7u9jmJEkbMcw+gqOBx1fVqqp6dFXtXlWPXmyDSR4EHALsDjwC2DHJi/unq6o1VTVdVdNTU1OLbU6StBHDBMEVwC3L2OZBwPeqaraq7gA+Azx1GecvSdoEw+wjeCPw9XYfwW1zA6vqVYts8/vAU5LsANwKHMgSupokSUszTBD8PXAGcAFw91IbrKpzkpwMnAfcCXwTWLPU+UqSFmeYILizqv5kORutqmOBY5dznpKkxRlmH8Ha9giehydZOfc38sokSWMxzBbB77X/39gzrIBFHzkkSdp8bDQIqspLTkvSVmzBIEhyQFWdkeQF842vqs+MrixJ0rgM2iJ4Bs3RQs+dZ1zRHP8vSdrCDQqC8wGq6sgx1SJJmoBBRw29aWxVSJImxh+vl6SOG9Q19Lgk588zPEBV1Z4jqkmSNEaDguB7zL+jWJK0FRkUBLdX1VVjq0SSNBGD9hF8bWxVSJImZsEgqKpXjLMQSdJkeNSQJHWcQSBJHTfoWkP91xgq4AZgQ1XdONKqJEljM+ioofkOHV0J7JnkqKo6Y0Q1SZLGaMEgWOgaQ0keBXwK2HdURUmSxmeT9xG05xZsN4JaJEkTsMlBkGQP4LYR1CJJmoBBO4v/iWYHca+VwMOBF4+yKEnS+AzaWfz2vvsF/Ai4rKpuH11JkqRxGnRm8ZnAg4AnAyuq6itVddFyhECSByY5OcklSS5O8mtLnackaXEWDIIk7wVeAzwYeEuSNy9ju38LfLGqHgc8Ebh4GectSdoEG/vN4idW1V1JdgDOAt6y1AaTPKCd90sA2i0Mu5okaUIGHTV0e1XdBVBVt9D8IM1yeDQwC3wkyTeTfDDJjv0TJVmdZCbJzOzs7DI1LUnqNygIHpfk/Pbvgp77Fyzwy2XD2hbYG3hfVT0JuBl4Q/9EVbWmqqaranpqamoJzUmSBhnUNfRfRtTmNcA1VXVOe/9k5gkCSdJ4DNoi2A7Ypaqu6v0DdmNwgAxUVf8GXN2emAZwIPDtxc5PkrQ0g4LgncB8Vxm9tR23FK8ETmy7mPYC3rrE+UmSFmnQN/tVVXWvfQFVNZNk1VIaraoNwPRS5iFJWh6DtghWDBi3/XIXIkmajEFB8I0kL+0fmOQoYP3oSpIkjdOgrqFXA6ckeRG/WPFPA/cFnj/qwiRJ4zHoh2muA56aZH/gCe3gf/aXyTSf/PlynW+4dHVs/0VzJQ2y0cNAq2otsHYMtUiSJmCTf5hGkrR1MQgkqeMMAknqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMMAknqOINAkjrOIJCkjtvoL5SNSpJtgBng2qp6zggbGtmsF6X8GUVJm5dJbhH8MXDxBNuXJDGhIEiyC/DbwAcn0b4k6RcmtUXwTuB1wN0LTZBkdZKZJDOzs7Pjq0ySOmbsQZDkOcD1VbV+0HRVtaaqpqtqempqakzVSVL3TGKL4GnA85JcCXwCOCDJxyZQhySJCQRBVb2xqnapqlXAYcAZVfXicdehbks2rz9pkjyPQJI6bmLnEQBU1Tpg3SRrkKSuc4tAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI4zCCSp4wwCSeo4g0CSOs4gkKSOMwgkqeMmeq0hzS+b2eUoy99ZlrZqbhFIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgSR1nEEhSxxkEktRxYw+CJLsmWZvk4iQXJfnjcdcgSfqFSVxr6E7gtVV1XpKdgPVJTq+qb0+gFknqvLFvEVTVD6vqvPb2jcDFwCPHXYckqTHRfQRJVgFPAs6ZZ9zqJDNJZmZnZ8ddmiR1xsSCIMn9gU8Dr66qn/WPr6o1VTVdVdNTU1PjL1CSOmIiQZBkO5oQOLGqPjOJGiRJjUkcNRTgQ8DFVfU3425fknRPkzhq6GnA7wMXJNnQDvvTqvr8BGqRthjr1m1ev1y3337+ct3WYuxBUFVfBTavd7QkdZhnFktSxxkEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgSR1nEEhSxxkEktRxk/g9AkkdsS7rJl3CPexX+026hM2SWwSS1HEGgSR1nEEgSR1nEEhSxxkEktRxBoEkdZxBIEkdN5EgSHJwku8kuTzJGyZRgySpMfYgSLIN8B7g2cAvA4cn+eVx1yFJakxii+BXgcur6rtVdTvwCeCQCdQhSWIyl5h4JHB1z/1rgH37J0qyGljd3r0pyXfGUNsgOwM3LHkuydIrGd6y1JwtrOYct2XVC+N+WyzTe5ktbzmPt+Rlem8s7eGPGmaiSQTBfM+r7jWgag2wZvTlDCfJTFVNT7qOTWHNo7el1QvWPC5bUs2T6Bq6Bti15/4uwA8mUIckickEwTeAxybZPcl9gcOAUydQhySJCXQNVdWdSV4BfAnYBvhwVV007joWYbPpptoE1jx6W1q9YM3jssXUnKp7dc9LkjrEM4slqeMMAknqOIMASPLQJB9P8t0k65OcneT5SfZL8rme6f4iyZeS3C/JuvYyGRuSXNye97DUOu5q5/etJOcleWo7fFWSC+eZPknelOSyJJcmWZvk8T3jr0xyQTvPC5Ic0tPGJUmubS/zcUWSDyU5tZ3XtUlu63luH+ldDn01TCW5I8nL+obPtX1+kjOTDHU8c/vYE5J8r6fOY3vGrUtyr0Pykjw9ybnt9Jf0vh5Jjmuf09z83pfkPj3L8O72+d6W5Pok+7TjdklyS8+4q5Ls2o67x3tj1MtkUyQ5JslFbTsbkuzb8379VpKvJdmjnXbbJG9tX/cN7d8xo6hriLoHfQ5/muSb7fvx2Hb6BV+DMdd9V8+y25D2sjkZwTpiZKqq03805zWcDby8Z9ijgFcC+wGfa4cdA6wFtm/vrwOm29srgZ8A911iLTf13P5N4Mz29irgwnmmfwXweWCH9v5vAFcAK9r7VwI7t7f3AK4Cbmqf87nt8/4Tmp32s8AX2mkPAL4PHA/sSHPI71kL1Pw/gbOAdX3De9v+c+AD8zx2P+CEeYafALywvb0C+C6we/9y75n+YW29e7f3dwbWA7/d3j8OOLq9fR/gq8D+Pcvwzp5l+GWak4BWtMvohnZ+2wAbgPU9tX9uuZfJMryff619Xe/Xsywe0fd+XQ2c2t5+W7u8594zOwHHbcafwx2By4B9Br0GY679pgWG9y7zZVlHjOrPLYJmpXd7Vb1/bkBVXVVV75q7n+S1wG8Bz62qW+eZx/2Bm4G7lrGuB9C8cQZ5PfDKqroFoKpOA74OvGgj8zsA+DnwUeAxNB+oS4FfTbIDcDfwbeAPaU72uxTYYYEaDgdeC+yS5JELTHM2zRnli7Gi/X/zgGn+iCZQzgOoqhuA1wHzXdDwvu0855bF64Hb5pYh8A/Aj4C/oFlGN7XzvAv438CvtMtokFEvk0EeDtxQVbdBsyyqqv88na8Aj2mfx0tp3kM/b6e/saqOG0FdG7PRz2E77GaakP+lMde3VKNYRywbgwAeD5w3YPzTgJcDz66qm/rGnZjkfOA7wFvalcVSbD/XfQF8EHjLQhMmeQCwY1Vd0TdqhuY5zVmbplvpTOBN7bC55/xs4IL2/rk036of005zZ3t/H5qLA944Tw27Ag+rqnOBTwG/u0C5BwOfXei5LOD4JBtotkY+UVXXD5j28TQrh179y+E17fx+CFxaVRvmliHtme1JtqVZJt8C9p5nngfQBMhjWMCIl8kwTgN2TdNV+N4kz5xnmufSvO6PAb5fVfd6bSdgY59DAJI8GHgKsDkdcr59X9dQ72u+3OuIkTAI+iR5T9uP+o120OU0m62/Mc/kL6qqPYHdgKOXoc/31qraq6oeR7Oi+H/JJl+FJtzzkh37V9UTgF8B3g1sT/Nt+fdoVvQf6nlM72N/naY76f3Ax2m/Gfc5jGZlB83FAw/vG782yfXAQe08mgKTc9qV8geB5/V8gH6z57H/q6r2oun2OTDt/pIhn/Oc3mHvaOf3EGDHJIf1jNu+rWeGZpmc0/f4te34BwDXL9DWnEUtk+XSflnZh6b7Zxb4ZJKXtKNPbJ/H04Cj+x+b5Mj2dbh6bl/IpMzzOfz1JN+kCbq31eZ17tHc53bu75M945Z7HTESBkHzzWLvuTtV9UfAgcBUO+g6mm6hdyTZf74ZVNUszbeZe108b7Gq6mya/t2pBcb/DLg5yaP7Ru1N063TP/0VNM/lNuAlwCVV9cpqrgB7UVv7rjT7GKDpurgVeDILn/l9OPCSJFe20zwxyWN7xu9P0897EU23ylwt+7Yr5f9O01c99wH60jx130TT1/r0BWqgnX//DuR9mH853AF8EXjG3DIEft5TwyuBJ9K8nnPz3L+t9xU0/e39W2G9FrVMllNV3VVV66rq2Lbm32lHvah9jodW1dU0X3J2S7JT+7iPtM/zpzT7RMZpY5/Ds6rqSVW1T2/30ZZiFOuI5WQQwBnAiiT/o2fYPfqAq+pS4AXAx5Ls1T+Dtq/1SQxeQWySJI+j+TD+aMBkxwN/l2T79jEH0aww7/VNM8lDgN1pvs1+GdghyR+0o9cC/xk4t+0rvw9Nd9AJPX3n/fPbg6Zr6pFVtaqqVgF/RfON+D+0+1ReDfxBkpXDPPe+dral+fAMWrbvoVn57tU+5sHA/wH+ep75BXhqz/yOB+43zzJ8M837YMd2+DbA/2UzWCaDJNmjL3j2ojlI4F7a5/Eh4N1JVrSP34ZmP8q4bfRzuCUbxTpiOU3i6qOblaqqJIfSfON/Hc3m9M00OxF7p/tGkiOBU3u2DE5McitwP5oVRH+f8qaa66KAprvjiKq6q+0d2iPJNT3TvgZ4F/Ag4IIkdwH/BhzSt0N7bTtuO5qdp3/XPufnA+9N8maaFf/ngIckuYzmA3g78Kc98zmwr/2LgFP66v80TXfIPfZtVNUPk5xEs1N3wf0efY5P8iaaldKXgc/0jPvnJHe0t8+uqv+a5MXAB9pvtwHeWVX/1POY17TTbAecD7y3Hf4umjC41zJsl9F3+EVX0eeZ7DIZxv2BdyV5IM1+nstpuolOXmD6Y9r2L0xyI81W4EcZ84Ugh/0cbqZ6P7cAX6yquQMVlnsdMRJeYkKSOs6uIUnqOINAkjrOIJCkjjMIJKnjDAJJ6jiDQJI6ziCQpI77/+Vc54EMTXMRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, blobop, polarblobop, polar, spg, gpi, eb = plt.bar([0, 1, 2, 3, 4, 5, 6], [t2_gkb, t2_blobop, t2_gkbpolarblobop, t2_gkbpolar, t2_spg, t2_gpi, t2_eb])\n",
    "gkb.set_facecolor('r')\n",
    "blobop.set_facecolor('k')\n",
    "polarblobop.set_facecolor('g')\n",
    "polar.set_facecolor('b')\n",
    "spg.set_facecolor('y')\n",
    "gpi.set_facecolor('m')\n",
    "eb.set_facecolor('c')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5,6])\n",
    "ax.set_xticklabels(['GKB', 'BLOBOP', 'POLAR+BLOBOP', 'POLAR', 'SPG', 'GPI', 'EB'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Problem 3')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEICAYAAABMGMOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGldJREFUeJzt3Xu4JHV95/H3Ry6igEFkRO6j4oMrRBBGUFEziCKwKmhchSXxEnXUFbO6ulGDEeIt7hKjG1EJKkFXRNSIEkWFKCNeUJnB4RZFLkIYYJnhIndBhu/+UXW0p+k+UzNzuvsM8349Tz+nLr+u/nad7v501a+6KlWFJEmr8pBJFyBJWjcYGJKkTgwMSVInBoYkqRMDQ5LUiYEhSerEwJDWUJJjknx+mvlXJXnuOGuSRsnA0Hql/RC/O8kdSW5I8s9JNpt0XWsjyWFJLk1ya5JlST6b5BGTrksPPgaG1kcvrKrNgD2BpwLv7m+Qxrry/vgRsG9V/RHwOGBD4P2TLUkPRuvKG0KacVV1LfAtYDeAJAuTfCDJj4C7gMcl2TbJ6UluTnJ5ktf1LWaTJKcmuT3J+Ul2H/RYSR6S5J1JrkhyU5IvJdmynTc3SSV5dZJrktyS5A1JnprkwiS/SXLcNM/jmqq6sWfSCmDntVg10kAGhtZbSXYADgZ+3jP5z4EFwObA1cApwFJgW+ClwAeT7N/T/hDgy8CWwBeAryXZaMDD/SVwKPAn7bJuAT7e12Yf4AnAy4GPAkcBzwV2BV6W5E+meS7PTHIrcDvwp+39pRllYGh99LUkvwF+CHwf+GDPvJOq6pKqug94DPBM4B1V9duqWgJ8miZUpiyuqq9U1e+AfwA2AZ424DFfDxxVVUur6h7gGOClSTbsafO+9nHOBO4ETqmqZe2W0A+Apwx7QlX1w3aX1PbAscBVndeG1NGGq24iPegcWlX/NmTeNT3D2wI3V9XtPdOuBuYNal9V9yeZ2hrptxNwWpL7e6atALbuGb+hZ/juAeOr7JyvqmuTfBv4Ik0fjTRj3MKQVtZ7+ubrgC2TbN4zbUfg2p7xHaYG2k7y7dv79bsGOKiqtui5bdJuPcy0DYHHj2C5Ws8ZGNIQVXUN8GPg75JskuTJwGuAk3ua7ZXkJe2upbcA9wA/GbC444EPJNkJIMmcJIfMRJ1JjkiyY3tk107AB4DvzsSypV4GhjS9w4G5NFsNpwFHV9VZPfO/TtNJfQtN38ZL2v6Mfv8HOB04M8ntNKGyzwzV+CSaYLuD5hDbS4H+o7mktRYvoCRJ6sItDElSJwaGJKkTA0OS1ImBIUnq5EH1w72tttqq5s6dO+kyJGmdsXjx4hurak6Xtg+qwJg7dy6LFi2adBmStM5IcnXXtu6SkiR1YmBIkjoxMCRJnRgYkqRODAxJUicGhiSpEwNDktSJgSFJ6sTAkCR18qD6pbckjUMWLpx0CSup+fPH8jhuYUiSOjEwJEmdGBiSpE4MDElSJwaGJKkTA0OS1ImBIUnqxMCQJHViYEiSOjEwJEmdGBiSpE5Gdi6pJCcCLwCWVdVu7bRTgV3aJlsAv6mqPQbc9yrgdmAFcF9VzRtVnZKkbkZ58sGTgOOAz01NqKqXTw0n+TBw6zT336+qbhxZdZKk1TKywKiqc5LMHTQvSYCXAc8Z1eNLkmbWpPowngXcUFWXDZlfwJlJFidZMN2CkixIsijJouXLl894oZKkxqQC43DglGnm71tVewIHAW9K8uxhDavqhKqaV1Xz5syZM9N1SpJaYw+MJBsCLwFOHdamqq5r/y4DTgP2Hk91kqRhJrGF8Vzgl1W1dNDMJJsm2XxqGDgAuHiM9UmSBhhZYCQ5BTgX2CXJ0iSvaWcdRt/uqCTbJjmjHd0a+GGSC4CfAd+sqm+Pqk5JUjejPErq8CHTXzVg2nXAwe3wlcDuo6pLkrRm/KW3JKkTA0OS1ImBIUnqxMCQJHViYEiSOjEwJEmdGBiSpE4MDElSJwaGJKkTA0OS1ImBIUnqxMCQJHViYEiSOjEwJEmdGBiSpE4MDElSJwaGJKkTA0OS1ImBIUnqZGSBkeTEJMuSXNwz7Zgk1yZZ0t4OHnLfA5NcmuTyJO8cVY2SpO5GuYVxEnDggOkfqao92tsZ/TOTbAB8HDgIeBJweJInjbBOSVIHIwuMqjoHuHkN7ro3cHlVXVlV9wJfBA6Z0eIkSattEn0YRya5sN1l9cgB87cDrukZX9pOGyjJgiSLkixavnz5TNcqSWqNOzA+CTwe2AO4HvjwgDYZMK2GLbCqTqiqeVU1b86cOTNTpSTpAcYaGFV1Q1WtqKr7gU/R7H7qtxTYoWd8e+C6cdQnSRpurIGRZJue0RcDFw9odh7whCSPTbIxcBhw+jjqkyQNt+GoFpzkFGA+sFWSpcDRwPwke9DsYroKeH3bdlvg01V1cFXdl+RI4DvABsCJVXXJqOqUJHUzssCoqsMHTP7MkLbXAQf3jJ8BPOCQW0nS5PhLb0lSJwaGJKkTA0OS1ImBIUnqxMCQJHViYEiSOjEwJEmdGBiSpE4MDElSJwaGJKkTA0OS1ImBIUnqxMCQJHViYEiSOjEwJEmdGBiSpE4MDElSJwaGJKmTkQVGkhOTLEtycc+0Y5P8MsmFSU5LssWQ+16V5KIkS5IsGlWNkqTuRrmFcRJwYN+0s4DdqurJwK+Ad01z//2qao+qmjei+iRJq2FkgVFV5wA39007s6rua0d/Amw/qseXJM2sSfZh/AXwrSHzCjgzyeIkC6ZbSJIFSRYlWbR8+fIZL1KS1JhIYCQ5CrgPOHlIk32rak/gIOBNSZ49bFlVdUJVzauqeXPmzBlBtZIkmEBgJHkl8ALgiKqqQW2q6rr27zLgNGDv8VUoSRpkrIGR5EDgHcCLququIW02TbL51DBwAHDxoLaSpPEZ5WG1pwDnArskWZrkNcBxwObAWe0hs8e3bbdNckZ7162BHya5APgZ8M2q+vao6pQkdbPhqBZcVYcPmPyZIW2vAw5uh68Edh9VXZKkNdNpCyPJM5O8uh2ek+Sxoy1LkjTbrDIwkhxN0+8w9SO7jYDPj7IoSdLs02UL48XAi4A74fe7jzYfZVGSpNmnS2Dc2x7+WvD7I5ckSeuZLoHxpST/BGyR5HXAvwGfGm1ZkqTZZpVHSVXV3yd5HnAbsAvwnqo6a+SVSZJmlU6H1VbVWUl+OtU+yZZVdfMq7iZJehBZZWAkeT3wXuBu4H4gNP0ZjxttaZKk2aTLFsbbgV2r6sZRFyNJmr26dHpfAQw875Mkaf3RZQvjXcCP2z6Me6YmVtVfjqwqSdKs0yUw/gn4HnARTR+GJGk91CUw7quq/zHySiRJs1qXPoyz28ugbpNky6nbyCuTJM0qXbYw/mv791090zysVpLWM11+6e2pzCVJwwMjyXOq6ntJXjJoflV9dXRlSZJmm+m2MJ5Nc3TUCwfMK8DAkKT1yHSBcSFAVb16TRee5ETgBcCyqtqtnbYlcCowF7gKeFlV3TLgvq8E3t2Ovr+qPrumdUiS1t50R0m9e5p5XZ0EHNg37Z3Ad6vqCcB32/GVtKFyNLAPsDdwdJJHzkA9kqQ11Oma3muqqs4B+s9qewgwtbXwWeDQAXd9PnBWVd3cbn2cxQODR5I0RtPtknpikgsHTA9QVfXkNXzMravqepqFXJ/k0QPabAdc0zO+tJ32wGKSBcACgB133HENS5Ikrcp0gfFrBnd4j0MGTKtBDavqBOAEgHnz5g1sI0lae9MFxr1VdfUIHvOGJNu0WxfbAMsGtFkKzO8Z3x5YOIJaJEkdTdeH8aMRPebpwCvb4VcCXx/Q5jvAAUke2XZ2H9BOkyRNyNDAqKoj13bhSU4BzgV2SbI0yWuADwHPS3IZ8Lx2nCTzkny6feybgfcB57W393pJWEmarE7X9F5TVXX4kFn7D2i7CHhtz/iJwIkjKk2StJpGelitJOnBY7pzSfWfQ6qAG4ElVXX7SKuSJM060+2SGnRI7ZbAk5O8pqq+N6KaJEmz0NDAGHYOqSQ7AV+iOW2HJGk9sdp9GO1vMzYaQS2SpFlstQMjyS7APSOoRZI0i03X6f2vPPB0HFsC2wB/NsqiJEmzz3Sd3n/fN17ATcBlVXXv6EqSJM1G0/3S+/vAI4GnAptU1TlVdYlhIUnrp6GBkeQTwFuBRwHvS/I3Y6tKkjTrrOqa3rtX1YokDwd+QHN+J0nSemi6o6TuraoVAFV1F4OvUSFJWk90veJegMe342t7xT1J0jpousD4T2OrQpI0600XGBvRXH97pQspJXkWcN1Iq5IkzTrT9WF8FBh0Vtq723mSpPXIdIExt6ou7J/YXuho7sgqkiTNStMFxibTzHvYTBciSZrdpguM85K8rn9ie13uxaMrSZI0G03X6f0W4LQkR/CHgJgHbAy8eE0fsD3b7ak9kx4HvKeqPtrTZj7wdeDX7aSvVtV71/QxJUlrb7oLKN0APCPJfsBu7eRvru2V9qrqUmAPgCQbANcCpw1o+oOqesHaPJYkaeZMt4UBQFWdDZw9osffH7iivSiTJGkWW+0LKM2ww4BThsx7epILknwrya7DFpBkQZJFSRYtX758NFVqvZXMrps0SRMLjCQbAy8Cvjxg9vnATlW1O/Ax4GvDllNVJ1TVvKqaN2fOnNEUK0ma6BbGQcD5bV/JSqrqtqq6ox0+A9goyVbjLlCS9AeTDIzDGbI7KsljkmYDPMneNHXeNMbaJEl9VtnpPQrt9TWeB7y+Z9obAKrqeOClwBuT3EdzKpLDqqr/+uKSpDGaSGC019d4VN+043uGjwOOG3ddkqThJn2UlCRpHWFgSJI6MTAkSZ0YGJKkTibS6a31U/52dv1UuY72wDtpdbiFIUnqxMCQJHViYEiSOjEwJEmdGBiSpE4MDElSJwaGJKkTf4cxZbZdzsyT80qaZdzCkCR1YmBIkjoxMCRJndiHsQ7LLOp38YKI0oOfWxiSpE4mFhhJrkpyUZIlSRYNmJ8k/5jk8iQXJtlzEnVKkhqT3iW1X1XdOGTeQcAT2ts+wCfbv5KkCZjNu6QOAT5XjZ8AWyTZZtJFSdL6apKBUcCZSRYnWTBg/nbANT3jS9tpK0myIMmiJIuWL18+olIlSZMMjH2rak+aXU9vSvLsvvmDDgF6wKE4VXVCVc2rqnlz5swZRZ2SJCYYGFV1Xft3GXAasHdfk6XADj3j2wPXjac6SVK/iQRGkk2TbD41DBwAXNzX7HTgFe3RUk8Dbq2q68dcqiSpNamjpLYGTmt/eLYh8IWq+naSNwBU1fHAGcDBwOXAXcCrJ1SrJIkJBUZVXQnsPmD68T3DBbxpnHVJkoabzYfVSpJmEQNDktSJgSFJ6sTAkCR1YmBIkjoxMCRJnRgYkqRODAxJUicGhiSpEwNDktSJgSFJ6sTAkCR1YmBIkjoxMCRJnRgYkqRODAxJUicGhiSpEwNDktTJ2AMjyQ5Jzk7yiySXJPnvA9rMT3JrkiXt7T3jrlOStLJJXNP7PuBtVXV+ks2BxUnOqqp/72v3g6p6wQTqkyQNMPYtjKq6vqrOb4dvB34BbDfuOiRJq2eifRhJ5gJPAX46YPbTk1yQ5FtJdp1mGQuSLEqyaPny5SOqVJI0scBIshnwL8Bbquq2vtnnAztV1e7Ax4CvDVtOVZ1QVfOqat6cOXNGV7AkrecmEhhJNqIJi5Or6qv986vqtqq6ox0+A9goyVZjLlOS1GMSR0kF+Azwi6r6hyFtHtO2I8neNHXeNL4qJUn9JnGU1L7AnwMXJVnSTvtrYEeAqjoeeCnwxiT3AXcDh1VVTaBWSVJr7IFRVT8Esoo2xwHHjaciSVIX/tJbktSJgSFJ6sTAkCR1YmBIkjoxMCRJnRgYkqRODAxJUicGhiSpEwNDktSJgSFJ6sTAkCR1YmBIkjoxMCRJnRgYkqRODAxJUicGhiSpEwNDktTJJC7RKkkrWZiFky7h9+bX/EmXMGu5hSFJ6mQigZHkwCSXJrk8yTsHzH9oklPb+T9NMnf8VUqSeo09MJJsAHwcOAh4EnB4kif1NXsNcEtV7Qx8BPhf461SktRvEn0YewOXV9WVAEm+CBwC/HtPm0OAY9rhrwDHJUlV1TgLldZFCxdm0iWsZP5837YPFpMIjO2Aa3rGlwL7DGtTVfcluRV4FHBj/8KSLAAWtKN3JLl0xitePVsxoM7VlrG96Wek3oyvXpipmo9ZB2sebxbMzGuZdeu1PL5ygZl6Xazd3Xfq2nASgTHoufV/BenSpplYdQJwwtoWNVOSLKqqeZOuo6t1rV6w5nFZ12pe1+qFda/mSXR6LwV26BnfHrhuWJskGwJ/BNw8luokSQNNIjDOA56Q5LFJNgYOA07va3M68Mp2+KXA9+y/kKTJGvsuqbZP4kjgO8AGwIlVdUmS9wKLqup04DPA/01yOc2WxWHjrnMtzJrdYx2ta/WCNY/LulbzulYvrGM1xy/ukqQu/KW3JKkTA0OS1ImBsRqSbJ3kC0muTLI4yblJXpxkfpJv9LR7f5LvtKc4WdieBmVJkl+0vxtZ2zpWtMu7IMn5SZ7RTp+b5OIB7ZPk3UkuS/KrJGcn2bVn/lVJLmqXeVHP8i9O8o32dlnb7op2+IokX0lya5KfJ7l62G9gksxJ8rskr++bPvW4Fyb5fpLOx4MnOSnJr9s6f5nk6J55C5M84FDFJM9M8rO2/S97/xdJjklybc/yPpnkIT3r7/4k97S3ZUn2audtn+SunnlXJ5k6wm+l18Wo18nqSHJUkkvax1mSZJ+e1+oFSX6UZJe27YZJPtj+35e0t6NGUVfH2qd7H069Hn8x9ZqY7v8w5rpX9Ky/JWlPi5QRfEaMTFV563Cj+W3IucAbeqbtBLwZmA98o512FHA28LB2fCEwrx3eErgF2Hgta7mjZ/j5wPfb4bnAxQPaHwmcATy8HT8AuALYpB2/CtiqHd4FuL/nOd8InNIO/ww4BziW5oCFbwJXtG0PBO4E9hrw+P8N+AGwsG967+P+LfCpAfedD5w0YPpJwEvb4U2AK4HH9q/znvaPAf4D2LMd3wpYDPzndvwY4O3t8EOAHwL79ay/+3rW33fb9bJJu05ubJe3AbAEWNxT+zeG/A/XeJ3MwGv56TSv5Yf2rItt+16rC4DT2+EPtet76vWyOXDMLH8fbgpcBuw13f9hzLXfMWR673qfkc+IUd3cwujuOcC9VXX81ISqurqqPjY1nuRtwMHAC6vq7gHL2IzmQ3XFDNb1CJoX2HTeAby5qu4CqKozgR8DRwxZ3tSREM9pl31LO/xb4AXAXwAPpTkn2A5JHt7O+w3w+AHLPBx4G7B9ku2G1HguzS/818Qm7d87p2nzJprgOR+gqm4E/gp4wMkvgY3bZU6t13cA90ytP+DLwE3A+2me9x3tMlcA7wX+uF0n0xn1OpnONsCNVXUPNOuiqvp/C3UOsHP7PF5H8/r5bdv+9qo6ZgR1dbHK92E77U6aLwSDXo+z2Sg+I2aMgdHdrsD508zfF3gDcFBV3dE37+QkFwKXAu9rP1jWxsOmdp0AnwbeN6xhkkcAm1bVFX2zFtE8pylnp9md9X3g3nbaH9O8Ri5q2y6uqttovqnvDNwF3N0OPwJ4JHBJ3+PvADymqn4GfAl4+ZBSDwS+Nt2THuDYJEtofuj5xapaNk3bXWk+QHr1r4O3tsu7HvhVVS2ZWn+0IZrmh6QHARcAew5Y5lTI7jyskBGvky7OpAn6XyX5RJI/GdDmhTT/952B/6iq20dQx5pY1fsQgCSPAp5G3+txwh7Wt0uq9/8+058RI2FgrKEkH2/39Z7XTrqcZnP5gAHNj6iqJwM7Am+fgf3Sd1fVHlX1RJoPlc8lq32WobDy6Vb2q6rdaEJik/bF+3bgNprfxfS27x3eDDiVZjfV5VXV/wY9jOZDEeCLNN+se52dZBnwXOALvy+uOa39EppAfFHPm+z5Pff9n1W1B83upv3T9uV0fL5Teqd9pF3eo4FNk/T+/udhbT2LaALzp333P7ud/whg2ZDHmrJG62SmtF9o9qLZ7bQcODXJq9rZJ7fPY1+a//9Kkry6/T9cM9VXM0kD3ofPSvJzmlD80IDX4yRNvW+nbqf2zJvpz4iRMDC6u4TmGyUAVfUmYH9gTjvpBprdUR9Jst+gBVTVcppvR/0nW1xjVXUuzT7oOUPm3wbcmeRxfbP2ZOUzBE+1vwK4H3gt8Crg9qq6l+b5z2u/ce9A0wfycJpN572A19N8kPY7HHhVkqtofsG/e5In9Mzfj2Yf9CU0u3Om6tin/fB+Lc2+9Kk32XcG1HwHzX7gZw5aB61LgP6O8L2GrIPfAd8Gnj21/oDf9tTwZmB3mv/l1DL3a+s9kqY/oH+LrtcarZOZVFUrqmphVR3d1vyn7awj2ud4aFVdQ/NFaMckm7f3++f2ed5K02czbqt6H/6gqp5SVXv17rZaV4ziM2ImGRjdfY/mm/cbe6attJ+6qn4FvAT4fJI9+hfQ7g9+CtN/mKyWJE+keePeNE2zY4F/TPKw9j7PpflwfcC31ySPpvk2fjVN5+7Dk7xiapjmA+4k4B6ajttrevbt9y9rF5rdYdtV1dyqmgv8HX2/3G/7e94CvCLJlt2e+UqPsyHNG2y69fpxmg/pPdr7PIrmOiv/e8DyAjyjZ3nHAg8dsP7+hmadbNpO3wD4ME1fyUTXyXSS7NIXUHvQ/L8foH0en6G5xMAm7f03oOnnmYRVvg/XZaP4jJhJXtO7o6qqJIfSbEH8Fc2m/J00HaK97c5L8mrg9J4tjZOT3E3TUXxSVfXv915dU7tHoPlwf2VVrWj3Su2SZGlP27cCH6PpX7goyQrg/wGH9HXMn93O24imU/EGgCQvBj5B8+G4Ec1p5rcHDqXZj997KO3+fY99CXBaX+3/QrMbZqV+l6q6PskpNJ3TQ/tk+hyb5N00H17fBb7aM++bSX7XDp9bVf8lyZ8Bn2q/LQf4aFX9a8993tq22Qi4sH3e0Ky/Yxmw/tr1cyl/2EV1BvDXPcsc9zrpYjPgY0m2oDn663Ka3VNfGdL+qPbxL05yO02/1Wd54ElDR67r+3CW6n3fAny7qqYOupjpz4iR8NQgkqRO3CUlSerEwJAkdWJgSJI6MTAkSZ0YGJKkTgwMSVInBoYkqZP/D5UGQKqVZp6aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gkb, blobop, polarblobop, polar, spg, gpi, eb = plt.bar([0, 1, 2, 3, 4, 5, 6], [t3_gkb, t3_blobop, t3_gkbpolarblobop, t3_gkbpolar, t3_spg, t3_gpi, t3_eb])\n",
    "gkb.set_facecolor('r')\n",
    "blobop.set_facecolor('k')\n",
    "polarblobop.set_facecolor('g')\n",
    "polar.set_facecolor('b')\n",
    "spg.set_facecolor('y')\n",
    "gpi.set_facecolor('m')\n",
    "eb.set_facecolor('c')\n",
    "ax = plt.gca()\n",
    "ax.set_xticks([0,1,2,3,4,5,6])\n",
    "ax.set_xticklabels(['GKB', 'BLOBOP', 'POLAR+BLOBOP', 'POLAR', 'SPG', 'GPI', 'EB'])\n",
    "#ax.set_ylim([0, 100])\n",
    "ax.set_ylabel('CPU Time')\n",
    "ax.set_title('Problem 3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}