examples/performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b6c23dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mip.constants import OptimizationStatus\n",
    "from scipy.spatial import distance_matrix\n",
    "from sklearn.datasets import make_moons\n",
    "\n",
    "from location_allocation import (\n",
    "    MaximizeCoverage,\n",
    "    MaximizeCoverageCapacitated,\n",
    "    MaximizeCoverageMinimizeCost,\n",
    "    MaximizeCoverageMinimizeFacilities,\n",
    ")\n",
    "import utils\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "plt.rcParams.update({\"figure.figsize\": [12, 8] })\n",
    "\n",
    "\n",
    "def make_colors(status):\n",
    "    colors = []\n",
    "    for s in status:\n",
    "        if s == OptimizationStatus.OPTIMAL:\n",
    "            colors.append(\"green\")\n",
    "        elif s == OptimizationStatus.FEASIBLE:\n",
    "            colors.append(\"orange\")\n",
    "        elif s == OptimizationStatus.INFEASIBLE:\n",
    "            colors.append(\"salmon\")\n",
    "        elif s == OptimizationStatus.NO_SOLUTION_FOUND:\n",
    "            colors.append(\"red\")\n",
    "    return colors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "452ee883",
   "metadata": {},
   "source": [
    "## Problem Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b266dcdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_points = [100, 300, 500, 700, 1000, 1500]\n",
    "custom_markers = [\n",
    "    Line2D(range(1), range(1), color=\"white\", markerfacecolor=\"green\", marker=\"o\", markersize=7),\n",
    "    Line2D(range(1), range(1), color=\"white\", markerfacecolor=\"orange\", marker=\"o\", markersize=7),\n",
    "    Line2D(range(1), range(1), color=\"white\", markerfacecolor=\"salmon\", marker=\"o\", markersize=7),\n",
    "    Line2D(range(1), range(1), color=\"white\", markerfacecolor=\"red\", marker=\"o\", markersize=7),\n",
    "]\n",
    "\n",
    "max_mip_gap = 0.1\n",
    "max_seconds = 2000\n",
    "\n",
    "problems = [\n",
    "    {\n",
    "        \"title\": \"Maximize Coverage (points, facilities, facilities to be sited)\",\n",
    "        \"filename\": \"maximize_coverage.png\",\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Maximize Capacitated Coverage (points, facilities, facilities to be sited)\",\n",
    "        \"filename\": \"maximize_coverage_capacitated.png\",\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Maximize Coverage Minimize Facilities (points, facilities, facilities to be sited)\",\n",
    "        \"filename\": \"maximize_coverage_minimize_facilities.png\",\n",
    "    },\n",
    "    {\n",
    "        \"title\": \"Maximize Coverage Minimize Cost (points, facilities, facilities to be sited)\",\n",
    "        \"filename\": \"maximize_coverage_minimize_cost.png\",\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a886207b",
   "metadata": {},
   "source": [
    "## Run Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92a0e270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cgl0002I 44622 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 49 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 855 rows, 1908 columns (1908 integer (1908 of which binary)) and 4104 elements\n",
      "Coin3009W Conflict graph built in 0.001 seconds, density: 0.049%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 100\n",
      "Cbc0012I Integer solution of 100 found by Reduced search after 0 iterations and 0 nodes (0.50 seconds)\n",
      "Cbc0012I Integer solution of -517 found by DiveCoefficient after 0 iterations and 0 nodes (0.52 seconds)\n",
      "Cbc0012I Integer solution of -555 found by DiveCoefficient after 276 iterations and 0 nodes (0.72 seconds)\n",
      "Cbc0031I 149 added rows had average density of 6.8053691\n",
      "Cbc0013I At root node, 149 cuts changed objective from -600.34126 to -555 in 4 passes\n",
      "Cbc0014I Cut generator 0 (Probing) - 1249 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.010 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 1 (Gomory) - 45 row cuts average 61.0 elements, 0 column cuts (0 active)  in 0.004 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 2 (Knapsack) - 4 row cuts average 15.5 elements, 0 column cuts (0 active)  in 0.007 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 3 (Clique) - 118 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.004 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 4 (OddWheel) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.113 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 37 row cuts average 22.4 elements, 0 column cuts (0 active)  in 0.002 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 6 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 7 (TwoMirCuts) - 59 row cuts average 58.5 elements, 0 column cuts (0 active)  in 0.011 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 8 (ZeroHalf) - 12 row cuts average 27.2 elements, 0 column cuts (0 active)  in 0.004 seconds - new frequency is 1\n",
      "Cbc0001I Search completed - best objective -555, took 276 iterations and 0 nodes (0.72 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.78   (Wallclock seconds):       0.92\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 3 (-98) rows, 4 (-98) columns and 6 (-99) elements\n",
      "Clp1000I sum of infeasibilities 0 - average 0, 3 fixed columns\n",
      "Coin0506I Presolve 0 (-3) rows, 0 (-4) columns and 0 (-6) elements\n",
      "Clp0000I Optimal - objective value -0\n",
      "Clp0000I Optimal - objective value -0\n",
      "Coin0511I After Postsolve, objective 0, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -0 Dual inf 2.999997 (3)\n",
      "Clp0000I Optimal - objective value 2\n",
      "Clp0000I Optimal - objective value 2\n",
      "Clp0000I Optimal - objective value 2\n",
      "Coin0511I After Postsolve, objective 2, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 2 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 97 variables fixed\n",
      "Cgl0004I processed model has 3 rows, 4 columns (4 integer (4 of which binary)) and 6 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 19.444%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
      "Cbc0038I Solution found of -2\n",
      "Cbc0038I Before mini branch and bound, 4 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)\n",
      "Cbc0038I After 0.00 seconds - Feasibility pump exiting with objective of -2 - took 0.00 seconds\n",
      "Cbc0012I Integer solution of -2 found by feasibility pump after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0001I Search completed - best objective -2, took 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 36 (-265) rows, 40 (-266) columns and 75 (-266) elements\n",
      "Clp1000I sum of infeasibilities 0 - average 0, 0 fixed columns\n",
      "Coin0506I Presolve 36 (0) rows, 40 (0) columns and 75 (0) elements\n",
      "Clp0006I 0  Obj 27.999155 Dual inf 0.349965 (35)\n",
      "Clp0029I End of values pass after 40 iterations\n",
      "Clp0000I Optimal - objective value 28\n",
      "Clp0000I Optimal - objective value 28\n",
      "Coin0511I After Postsolve, objective 28, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 28\n",
      "Clp0000I Optimal - objective value 28\n",
      "Clp0000I Optimal - objective value 28\n",
      "Clp0000I Optimal - objective value 28\n",
      "Coin0511I After Postsolve, objective 28, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 28 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 265 variables fixed\n",
      "Cgl0004I processed model has 36 rows, 40 columns (40 integer (40 of which binary)) and 75 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 2.315%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
      "Cbc0038I Solution found of -28\n",
      "Cbc0038I Before mini branch and bound, 40 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)\n",
      "Cbc0038I After 0.00 seconds - Feasibility pump exiting with objective of -28 - took 0.00 seconds\n",
      "Cbc0012I Integer solution of -28 found by feasibility pump after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0001I Search completed - best objective -28, took 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 109 (-392) rows, 117 (-393) columns and 233 (-393) elements\n",
      "Clp1000I sum of infeasibilities 0 - average 0, 7 fixed columns\n",
      "Coin0506I Presolve 103 (-6) rows, 110 (-7) columns and 214 (-19) elements\n",
      "Clp0006I 0  Obj 82.99721 Dual inf 1.009899 (101)\n",
      "Clp0029I End of values pass after 110 iterations\n",
      "Clp0000I Optimal - objective value 83\n",
      "Clp0000I Optimal - objective value 83\n",
      "Coin0511I After Postsolve, objective 83, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 83\n",
      "Clp0000I Optimal - objective value 83\n",
      "Clp0000I Optimal - objective value 83\n",
      "Clp0000I Optimal - objective value 83\n",
      "Coin0511I After Postsolve, objective 83, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 83 - 0 iterations time 0.012, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 392 variables fixed\n",
      "Cgl0004I processed model has 109 rows, 117 columns (117 integer (117 of which binary)) and 233 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.789%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
      "Cbc0038I Solution found of -83\n",
      "Cbc0038I Before mini branch and bound, 117 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)\n",
      "Cbc0038I After 0.00 seconds - Feasibility pump exiting with objective of -83 - took 0.00 seconds\n",
      "Cbc0012I Integer solution of -83 found by feasibility pump after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0001I Search completed - best objective -83, took 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 153 (-548) rows, 165 (-549) columns and 338 (-549) elements\n",
      "Clp1000I sum of infeasibilities 5.1096e-09 - average 3.33961e-11, 154 fixed columns\n",
      "Coin0506I Presolve 0 (-153) rows, 0 (-165) columns and 0 (-338) elements\n",
      "Clp0000I Optimal - objective value -0\n",
      "Clp0000I Optimal - objective value -0\n",
      "Coin0511I After Postsolve, objective 0, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -0 Dual inf 151.99985 (152)\n",
      "Clp0014I Perturbing problem by 0.001% of 1.0000006 - largest nonzero change 0 ( 0%) - largest zero change 2.8167904e-05\n",
      "Clp0000I Optimal - objective value 143\n",
      "Clp0000I Optimal - objective value 143\n",
      "Clp0000I Optimal - objective value 143\n",
      "Coin0511I After Postsolve, objective 143, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 143 - 0 iterations time 0.012, Presolve 0.00, Idiot 0.01\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 548 variables fixed\n",
      "Cgl0004I processed model has 153 rows, 165 columns (165 integer (165 of which binary)) and 338 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.542%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
      "Cbc0038I Solution found of -143\n",
      "Cbc0038I Before mini branch and bound, 165 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.01 seconds)\n",
      "Cbc0038I After 0.01 seconds - Feasibility pump exiting with objective of -143 - took 0.00 seconds\n",
      "Cbc0012I Integer solution of -143 found by feasibility pump after 0 iterations and 0 nodes (0.01 seconds)\n",
      "Cbc0001I Search completed - best objective -143, took 0 iterations and 0 nodes (0.01 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 293 (-708) rows, 309 (-711) columns and 613 (-711) elements\n",
      "Clp1000I sum of infeasibilities 1.47313e-11 - average 5.02776e-14, 294 fixed columns\n",
      "Coin0506I Presolve 0 (-293) rows, 0 (-309) columns and 0 (-613) elements\n",
      "Clp0000I Optimal - objective value -0\n",
      "Clp0000I Optimal - objective value -0\n",
      "Coin0511I After Postsolve, objective 0, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -0 Dual inf 291.99971 (292)\n",
      "Clp0014I Perturbing problem by 0.001% of 1.0064519 - largest nonzero change 0 ( 0%) - largest zero change 2.982246e-05\n",
      "Clp0000I Optimal - objective value 261\n",
      "Clp0000I Optimal - objective value 261\n",
      "Clp0000I Optimal - objective value 261\n",
      "Coin0511I After Postsolve, objective 261, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 261 - 0 iterations time 0.012, Presolve 0.00, Idiot 0.01\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 708 variables fixed\n",
      "Cgl0004I processed model has 293 rows, 309 columns (309 integer (309 of which binary)) and 613 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.308%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
      "Cbc0038I Solution found of -261\n",
      "Cbc0038I Before mini branch and bound, 309 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.01 seconds)\n",
      "Cbc0038I After 0.01 seconds - Feasibility pump exiting with objective of -261 - took 0.00 seconds\n",
      "Cbc0012I Integer solution of -261 found by feasibility pump after 0 iterations and 0 nodes (0.01 seconds)\n",
      "Cbc0001I Search completed - best objective -261, took 0 iterations and 0 nodes (0.01 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coin0506I Presolve 833 (-668) rows, 862 (-668) columns and 1919 (-668) elements\n",
      "Clp1000I sum of infeasibilities 2.52327e-09 - average 3.02914e-12, 123 fixed columns\n",
      "Coin0506I Presolve 723 (-110) rows, 739 (-123) columns and 1572 (-347) elements\n",
      "Clp0006I 0  Obj 699.96681 Dual inf 7.089291 (709)\n",
      "Clp0029I End of values pass after 739 iterations\n",
      "Clp0014I Perturbing problem by 0.001% of 1.0000001 - largest nonzero change 2.9976734e-05 ( 0.0014988367%) - largest zero change 2.9936918e-05\n",
      "Clp0000I Optimal - objective value 700\n",
      "Clp0000I Optimal - objective value 700\n",
      "Coin0511I After Postsolve, objective 700, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 700 Dual inf 28.999997 (3)\n",
      "Clp0014I Perturbing problem by 0.001% of 1.0000001 - largest nonzero change 2.9976734e-05 ( 0.0014988367%) - largest zero change 2.9868801e-05\n",
      "Clp0000I Optimal - objective value 700\n",
      "Clp0000I Optimal - objective value 700\n",
      "Clp0000I Optimal - objective value 700\n",
      "Coin0511I After Postsolve, objective 700, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 700 - 0 iterations time 0.062, Presolve 0.00, Idiot 0.06\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cgl0002I 668 variables fixed\n",
      "Cgl0004I processed model has 833 rows, 862 columns (862 integer (862 of which binary)) and 1919 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.101%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0038I Initial state - 0 integers unsatisfied sum - 0\n",
      "Cbc0038I Solution found of -700\n",
      "Cbc0038I Before mini branch and bound, 862 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.02 seconds)\n",
      "Cbc0038I After 0.02 seconds - Feasibility pump exiting with objective of -700 - took 0.00 seconds\n",
      "Cbc0012I Integer solution of -700 found by feasibility pump after 0 iterations and 0 nodes (0.02 seconds)\n",
      "Cbc0001I Search completed - best objective -700, took 0 iterations and 0 nodes (0.02 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.02   (Wallclock seconds):       0.02\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 8 (-295) rows, 9 (-293) columns and 20 (-886) elements\n",
      "Clp1000I sum of infeasibilities 0.001 - average 0.000125, 9 fixed columns\n",
      "Coin0506I Presolve 0 (-8) rows, 0 (-9) columns and 0 (-20) elements\n",
      "Clp0000I Optimal - objective value 4\n",
      "Clp0000I Optimal - objective value 4\n",
      "Coin0511I After Postsolve, objective 4, infeasibilities - dual 0 (0), primal 0.000999 (1)\n",
      "Coin0512I Presolved model was optimal, full model needs cleaning up\n",
      "Clp0006I 0  Obj 4 Primal inf 4.6234133e-06 (1) Dual inf 5.8012412e+10 (2)\n",
      "Clp0000I Optimal - objective value 4\n",
      "Clp0000I Optimal - objective value 4\n",
      "Clp0000I Optimal - objective value 4\n",
      "Coin0511I After Postsolve, objective 4, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 4 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 292 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 3 strengthened rows, 2 substitutions\n",
      "Cgl0004I processed model has 6 rows, 8 columns (8 integer (8 of which binary)) and 16 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 11.029%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I Warning: MIPstart solution is not valid, column names do not match, ignoring it.\n",
      "Cbc0006I The LP relaxation is infeasible or too expensive\n",
      "Coin0505I Presolved problem not optimal, resolve after postsolve\n",
      "Coin0505I Presolved problem not optimal, resolve after postsolve\n",
      "Coin0505I Presolved problem not optimal, resolve after postsolve\n",
      "Cgl0013I Postprocessed model is infeasible - possible tolerance issue - try without preprocessing\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 67 (-1999) rows, 110 (-1996) columns and 267 (-7251) elements\n",
      "Clp1000I sum of infeasibilities 6.37393e-08 - average 9.51333e-10, 65 fixed columns\n",
      "Coin0506I Presolve 7 (-60) rows, 6 (-104) columns and 15 (-252) elements\n",
      "Clp0006I 0  Obj -0\n",
      "Clp0000I Optimal - objective value 0\n",
      "Clp0000I Optimal - objective value 0\n",
      "Coin0511I After Postsolve, objective 0, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -0 Dual inf 3824.4604 (51)\n",
      "Clp0029I End of values pass after 3 iterations\n",
      "Clp0014I Perturbing problem by 0.001% of 51.614889 - largest nonzero change 0 ( 0%) - largest zero change 2.7540308e-05\n",
      "Clp0000I Optimal - objective value 31\n",
      "Clp0000I Optimal - objective value 31\n",
      "Clp0000I Optimal - objective value 31\n",
      "Coin0511I After Postsolve, objective 31, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 31 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 1996 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 14 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 67 rows, 110 columns (110 integer (110 of which binary)) and 270 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.872%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart solution provided values for 15 of 110 integer variables, 2 variables are still fractional.\n",
      "Cbc0038I Full problem 67 rows 110 columns, reduced to 0 rows 0 columns\n",
      "Cbc0045I Mini branch and bound defined values for remaining variables in 0.00 seconds.\n",
      "Cbc0045I MIPStart provided solution with cost -33\n",
      "Cbc0012I Integer solution of 15 found by Reduced search after 0 iterations and 0 nodes (0.03 seconds)\n",
      "Cbc0012I Integer solution of -19 found by DiveCoefficient after 0 iterations and 0 nodes (0.03 seconds)\n",
      "Cbc0011I Exiting as integer gap of 1 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -19, took 0 iterations and 0 nodes (0.03 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.03   (Wallclock seconds):       0.03\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 93 (-5357) rows, 163 (-5347) columns and 388 (-20142) elements\n",
      "Clp1000I sum of infeasibilities 0.00556633 - average 5.9853e-05, 70 fixed columns\n",
      "Coin0506I Presolve 52 (-41) rows, 89 (-74) columns and 221 (-167) elements\n",
      "Clp0006I 0  Obj 51.005566 Primal inf 0.00017302277 (3) Dual inf 4.3e+09 (86)\n",
      "Clp0029I End of values pass after 89 iterations\n",
      "Clp0000I Optimal - objective value 51\n",
      "Clp0000I Optimal - objective value 51\n",
      "Coin0511I After Postsolve, objective 51, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 51\n",
      "Clp0000I Optimal - objective value 51\n",
      "Clp0000I Optimal - objective value 51\n",
      "Clp0000I Optimal - objective value 51\n",
      "Coin0511I After Postsolve, objective 51, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 51 - 0 iterations time 0.012, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 5344 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 14 strengthened rows, 0 substitutions\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 1 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 93 rows, 163 columns (163 integer (163 of which binary)) and 391 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.587%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart solution provided values for 34 of 163 integer variables, 2 variables are still fractional.\n",
      "Cbc0038I Full problem 93 rows 163 columns, reduced to 0 rows 0 columns\n",
      "Cbc0045I Mini branch and bound defined values for remaining variables in 0.00 seconds.\n",
      "Cbc0045I MIPStart provided solution with cost -71\n",
      "Cbc0012I Integer solution of 32 found by Reduced search after 0 iterations and 0 nodes (0.08 seconds)\n",
      "Cbc0012I Integer solution of -43 found by DiveCoefficient after 0 iterations and 0 nodes (0.08 seconds)\n",
      "Cbc0001I Search completed - best objective -43, took 0 iterations and 0 nodes (0.08 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.08   (Wallclock seconds):       0.09\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 222 (-10112) rows, 413 (-10101) columns and 1002 (-38940) elements\n",
      "Clp1000I sum of infeasibilities 1.89955e-05 - average 8.55655e-08, 152 fixed columns\n",
      "Coin0506I Presolve 140 (-82) rows, 255 (-158) columns and 635 (-367) elements\n",
      "Clp0006I 0  Obj 143.00002 Dual inf 39.528346 (125)\n",
      "Clp0000I Optimal - objective value 143\n",
      "Clp0000I Optimal - objective value 143\n",
      "Coin0511I After Postsolve, objective 143, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 143\n",
      "Clp0000I Optimal - objective value 143\n",
      "Clp0000I Optimal - objective value 143\n",
      "Clp0000I Optimal - objective value 143\n",
      "Coin0511I After Postsolve, objective 143, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 143 - 0 iterations time 0.012, Presolve 0.01, Idiot 0.01\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 10098 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 26 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 222 rows, 413 columns (413 integer (413 of which binary)) and 1008 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.234%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 85\n",
      "Cbc0012I Integer solution of 85 found by Reduced search after 0 iterations and 0 nodes (0.14 seconds)\n",
      "Cbc0012I Integer solution of -117 found by DiveCoefficient after 0 iterations and 0 nodes (0.14 seconds)\n",
      "Cbc0001I Search completed - best objective -117, took 0 iterations and 0 nodes (0.14 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.15   (Wallclock seconds):       0.17\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 400 (-20201) rows, 828 (-20192) columns and 2172 (-78888) elements\n",
      "Clp1000I sum of infeasibilities 0.000398455 - average 9.96138e-07, 367 fixed columns\n",
      "Coin0506I Presolve 244 (-156) rows, 450 (-378) columns and 1132 (-1040) elements\n",
      "Clp0006I 0  Obj 264.00002 Primal inf 1.0883675e-05 (1) Dual inf 1.6002257e+10 (213)\n",
      "Clp0000I Optimal - objective value 264\n",
      "Clp0000I Optimal - objective value 264\n",
      "Coin0511I After Postsolve, objective 264, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 264\n",
      "Clp0000I Optimal - objective value 264\n",
      "Clp0000I Optimal - objective value 264\n",
      "Clp0000I Optimal - objective value 264\n",
      "Coin0511I After Postsolve, objective 264, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 264 - 0 iterations time 0.032, Presolve 0.01, Idiot 0.02\n",
      "\n",
      "Starting MIP optimization\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 20191 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 48 strengthened rows, 2 substitutions\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 2 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 398 rows, 827 columns (827 integer (827 of which binary)) and 2180 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.113%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 155\n",
      "Cbc0012I Integer solution of 155 found by Reduced search after 0 iterations and 0 nodes (0.30 seconds)\n",
      "Cbc0038I Full problem 398 rows 827 columns, reduced to 146 rows 266 columns\n",
      "Cbc0012I Integer solution of -216 found by RINS after 0 iterations and 0 nodes (0.32 seconds)\n",
      "Cbc0011I Exiting as integer gap of 7.661882 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -216, took 0 iterations and 0 nodes (0.33 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.33   (Wallclock seconds):       0.40\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 750 (-44951) rows, 1599 (-44931) columns and 4062 (-177528) elements\n",
      "Clp1000I sum of infeasibilities 0.0041811 - average 5.57479e-06, 939 fixed columns\n",
      "Coin0506I Presolve 321 (-429) rows, 609 (-990) columns and 1426 (-2636) elements\n",
      "Clp0006I 0  Obj 615.00251 Primal inf 0.0011594693 (27) Dual inf 3.8332581e+10 (450)\n",
      "Clp0029I End of values pass after 609 iterations\n",
      "Clp0000I Optimal - objective value 615\n",
      "Clp0000I Optimal - objective value 615\n",
      "Coin0511I After Postsolve, objective 615, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj 615\n",
      "Clp0000I Optimal - objective value 615\n",
      "Clp0000I Optimal - objective value 615\n",
      "Clp0000I Optimal - objective value 615\n",
      "Coin0511I After Postsolve, objective 615, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective 615 - 0 iterations time 0.072, Presolve 0.03, Idiot 0.03\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cgl0002I 44929 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 61 strengthened rows, 2 substitutions\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 3 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 748 rows, 1598 columns (1598 integer (1598 of which binary)) and 4070 elements\n",
      "Coin3009W Conflict graph built in 0.001 seconds, density: 0.059%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart solution provided values for 288 of 1598 integer variables, 4 variables are still fractional.\n",
      "Cbc0038I Full problem 748 rows 1598 columns, reduced to 408 rows 903 columns\n",
      "Cbc0038I Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
      "Cbc0045I Warning: mipstart values could not be used to build a solution.\n",
      "Cbc0038I Initial state - 5 integers unsatisfied sum - 0.988636\n",
      "Cbc0038I Pass   1: suminf.    0.96023 (5) obj. -490.688 iterations 207\n",
      "Cbc0038I Pass   2: suminf.    0.33523 (3) obj. -490 iterations 65\n",
      "Cbc0038I Pass   3: suminf.    0.27273 (3) obj. -490 iterations 1\n",
      "Cbc0038I Pass   4: suminf.    3.74703 (24) obj. -352 iterations 452\n",
      "Cbc0038I Pass   5: suminf.    3.15836 (22) obj. -352 iterations 89\n",
      "Cbc0038I Pass   6: suminf.    3.25647 (22) obj. -352 iterations 18\n",
      "Cbc0038I Pass   7: suminf.    5.20520 (22) obj. -292 iterations 251\n",
      "Cbc0038I Pass   8: suminf.    4.76484 (21) obj. -292 iterations 79\n",
      "Cbc0038I Pass   9: suminf.    4.81246 (21) obj. -292 iterations 17\n",
      "Cbc0038I Pass  10: suminf.    4.76484 (21) obj. -292 iterations 19\n",
      "Cbc0038I Pass  11: suminf.    5.89913 (23) obj. -240 iterations 294\n",
      "Cbc0038I Pass  12: suminf.    5.89913 (23) obj. -240 iterations 70\n",
      "Cbc0038I Pass  13: suminf.    5.72563 (22) obj. -240 iterations 10\n",
      "Cbc0038I Pass  14: suminf.    5.54196 (22) obj. -240 iterations 14\n",
      "Cbc0038I Pass  15: suminf.    5.54196 (22) obj. -240 iterations 7\n",
      "Cbc0038I Pass  16: suminf.    7.39001 (26) obj. -203.43 iterations 268\n",
      "Cbc0038I Pass  17: suminf.    6.52949 (24) obj. -203 iterations 65\n",
      "Cbc0038I Pass  18: suminf.    6.52949 (24) obj. -203 iterations 9\n",
      "Cbc0038I Pass  19: suminf.    6.96863 (24) obj. -203 iterations 263\n",
      "Cbc0038I Pass  20: suminf.    6.47033 (23) obj. -203 iterations 55\n",
      "Cbc0038I Pass  21: suminf.    6.47033 (23) obj. -203 iterations 2\n",
      "Cbc0038I Pass  22: suminf.    6.90580 (22) obj. -185 iterations 269\n",
      "Cbc0038I Pass  23: suminf.    6.90580 (22) obj. -185 iterations 55\n",
      "Cbc0038I Pass  24: suminf.    6.79049 (21) obj. -185 iterations 2\n",
      "Cbc0038I Pass  25: suminf.    6.61356 (20) obj. -185 iterations 4\n",
      "Cbc0038I Pass  26: suminf.    6.79049 (21) obj. -185 iterations 3\n",
      "Cbc0038I Pass  27: suminf.    7.11514 (23) obj. -190 iterations 269\n",
      "Cbc0038I Pass  28: suminf.    7.11514 (23) obj. -190 iterations 56\n",
      "Cbc0038I Pass  29: suminf.    6.62699 (21) obj. -190 iterations 6\n",
      "Cbc0038I Pass  30: suminf.    6.62699 (21) obj. -190 iterations 2\n",
      "Cbc0038I No solution found this major pass\n",
      "Cbc0038I Before mini branch and bound, 498 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Full problem 748 rows 1598 columns, reduced to 257 rows 542 columns\n",
      "Cbc0038I Mini branch and bound improved solution from 1.79769e+308 to -488 (0.74 seconds)\n",
      "Cbc0038I Round again with cutoff of -489.169\n",
      "Cbc0038I Reduced cost fixing fixed 14 variables on major pass 2\n",
      "Cbc0038I Pass  30: suminf.    0.96023 (5) obj. -490.687 iterations 0\n",
      "Cbc0038I Pass  31: suminf.    0.33523 (3) obj. -490 iterations 104\n",
      "Cbc0038I Pass  32: suminf.    0.27273 (3) obj. -490 iterations 1\n",
      "Cbc0038I Pass  33: suminf.    3.05436 (15) obj. -489.169 iterations 561\n",
      "Cbc0038I Pass  34: suminf.    2.35912 (13) obj. -489.169 iterations 33\n",
      "Cbc0038I Pass  35: suminf.    1.36002 (10) obj. -489.345 iterations 267\n",
      "Cbc0038I Pass  36: suminf.    0.97368 (9) obj. -489.169 iterations 269\n",
      "Cbc0038I Pass  37: suminf.    1.36005 (9) obj. -489.362 iterations 287\n",
      "Cbc0038I Pass  38: suminf.    0.97368 (9) obj. -489.169 iterations 266\n",
      "Cbc0038I Pass  39: suminf.    2.45142 (12) obj. -489.169 iterations 230\n",
      "Cbc0038I Pass  40: suminf.    1.58600 (9) obj. -489.41 iterations 262\n",
      "Cbc0038I Pass  41: suminf.    1.06459 (8) obj. -489.169 iterations 268\n",
      "Cbc0038I Pass  42: suminf.    1.58604 (8) obj. -489.429 iterations 270\n",
      "Cbc0038I Pass  43: suminf.    3.01420 (13) obj. -489.169 iterations 435\n",
      "Cbc0038I Pass  44: suminf.    1.43689 (9) obj. -489.169 iterations 253\n",
      "Cbc0038I Pass  45: suminf.    1.60108 (9) obj. -489.251 iterations 278\n",
      "Cbc0038I Pass  46: suminf.    1.43689 (9) obj. -489.169 iterations 260\n",
      "Cbc0038I Pass  47: suminf.    2.85494 (14) obj. -489.169 iterations 214\n",
      "Cbc0038I Pass  48: suminf.    2.09532 (11) obj. -489.538 iterations 295\n",
      "Cbc0038I Pass  49: suminf.    1.50892 (10) obj. -489.169 iterations 251\n",
      "Cbc0038I Pass  50: suminf.    1.44165 (10) obj. -489.169 iterations 4\n",
      "Cbc0038I Pass  51: suminf.    1.55344 (10) obj. -489.764 iterations 269\n",
      "Cbc0038I Pass  52: suminf.    1.55344 (10) obj. -489.764 iterations 1\n",
      "Cbc0038I Pass  53: suminf.    1.41801 (9) obj. -489.169 iterations 283\n",
      "Cbc0038I Pass  54: suminf.    1.34165 (9) obj. -489.169 iterations 7\n",
      "Cbc0038I Pass  55: suminf.    1.32428 (9) obj. -489.84 iterations 283\n",
      "Cbc0038I Pass  56: suminf.    1.29518 (9) obj. -489.847 iterations 9\n",
      "Cbc0038I Pass  57: suminf.    1.32711 (9) obj. -489.169 iterations 277\n",
      "Cbc0038I Pass  58: suminf.    1.24641 (8) obj. -489.169 iterations 6\n",
      "Cbc0038I Pass  59: suminf.    1.13371 (8) obj. -489.888 iterations 277\n",
      "Cbc0038I No solution found this major pass\n",
      "Cbc0038I Before mini branch and bound, 1332 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.88 seconds)\n",
      "Cbc0038I After 0.88 seconds - Feasibility pump exiting with objective of -488 - took 0.22 seconds\n",
      "Cbc0012I Integer solution of -488 found by feasibility pump after 0 iterations and 0 nodes (0.88 seconds)\n",
      "Cbc0011I Exiting as integer gap of 2.6875 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -488, took 0 iterations and 0 nodes (0.88 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.86   (Wallclock seconds):       0.98\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 13 (-291) rows, 13 (-291) columns and 40 (-1072) elements\n",
      "Clp1000I sum of infeasibilities 2.02954e-06 - average 1.56118e-07, 11 fixed columns\n",
      "Coin0506I Presolve 0 (-13) rows, 0 (-13) columns and 0 (-40) elements\n",
      "Clp0000I Optimal - objective value -1455\n",
      "Clp0000I Optimal - objective value -1455\n",
      "Coin0511I After Postsolve, objective -1455, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -1455 Dual inf 1423.0249 (5)\n",
      "Clp0000I Optimal - objective value -1455\n",
      "Clp0000I Optimal - objective value -1455\n",
      "Clp0000I Optimal - objective value -1455\n",
      "Coin0511I After Postsolve, objective -1455, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -1455 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 290 variables fixed\n",
      "Cgl0003I 0 fixed, 1 tightened bounds, 7 strengthened rows, 1 substitutions\n",
      "Cgl0004I processed model has 11 rows, 12 columns (12 integer (10 of which binary)) and 37 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 6.333%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 4\n",
      "Cbc0012I Integer solution of 4 found by Reduced search after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0012I Integer solution of -396 found by DiveCoefficient after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0001I Search completed - best objective -396, took 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 66 (-2009) rows, 99 (-2009) columns and 327 (-9009) elements\n",
      "Clp0006I 0  Obj 0 Primal inf 94.868329 (1) Dual inf 2.5302231e+10 (48)\n",
      "Clp0014I Perturbing problem by 0.001% of 106.35628 - largest nonzero change 9.9879042e-05 ( 9.6740694%) - largest zero change 0\n",
      "Clp0000I Optimal - objective value -6919\n",
      "Coin0511I After Postsolve, objective -6919, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -6919 - 102 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 2009 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 16 strengthened rows, 2 substitutions\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 2 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 64 rows, 98 columns (98 integer (96 of which binary)) and 322 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.917%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 86\n",
      "Cbc0012I Integer solution of 86 found by Reduced search after 0 iterations and 0 nodes (0.03 seconds)\n",
      "Cbc0038I Full problem 64 rows 98 columns, reduced to 13 rows 18 columns\n",
      "Cbc0012I Integer solution of -2473 found by RINS after 0 iterations and 0 nodes (0.04 seconds)\n",
      "Cbc0012I Integer solution of -3732 found by DiveCoefficient after 97 iterations and 0 nodes (0.12 seconds)\n",
      "Cbc0031I 35 added rows had average density of 3.6571429\n",
      "Cbc0013I At root node, 35 cuts changed objective from -4035.1152 to -3732 in 4 passes\n",
      "Cbc0014I Cut generator 0 (Probing) - 495 row cuts average 3.0 elements, 0 column cuts (0 active)  in 0.003 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 1 (Gomory) - 32 row cuts average 40.6 elements, 0 column cuts (0 active)  in 0.002 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.001 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 4 (OddWheel) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.001 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 4 row cuts average 8.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 6 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 7 (TwoMirCuts) - 36 row cuts average 36.5 elements, 0 column cuts (0 active)  in 0.005 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 8 (ZeroHalf) - 2 row cuts average 7.5 elements, 0 column cuts (0 active)  in 0.031 seconds - new frequency is -100\n",
      "Cbc0001I Search completed - best objective -3732, took 97 iterations and 0 nodes (0.12 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.09   (Wallclock seconds):       0.14\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 150 (-5261) rows, 254 (-5258) columns and 819 (-24741) elements\n",
      "Clp1000I sum of infeasibilities 0.000295498 - average 1.96998e-06, 209 fixed columns\n",
      "Coin0506I Presolve 24 (-126) rows, 38 (-216) columns and 130 (-689) elements\n",
      "Clp0006I 0  Obj -15888 Dual inf 31.220433 (34)\n",
      "Clp0029I End of values pass after 37 iterations\n",
      "Clp0000I Optimal - objective value -15888\n",
      "Clp0000I Optimal - objective value -15888\n",
      "Coin0511I After Postsolve, objective -15888, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -15888 Dual inf 22196.869 (96)\n",
      "Clp0014I Perturbing problem by 0.001% of 66.936171 - largest nonzero change 2.9709032e-05 ( 0.0029318069%) - largest zero change 2.9031261e-05\n",
      "Clp0000I Optimal - objective value -15888\n",
      "Clp0000I Optimal - objective value -15888\n",
      "Clp0000I Optimal - objective value -15888\n",
      "Coin0511I After Postsolve, objective -15888, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -15888 - 0 iterations time 0.012, Presolve 0.00, Idiot 0.01\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 5258 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 24 strengthened rows, 2 substitutions\n",
      "Cgl0004I processed model has 147 rows, 252 columns (252 integer (250 of which binary)) and 811 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.373%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 264\n",
      "Cbc0012I Integer solution of 264 found by Reduced search after 0 iterations and 0 nodes (0.08 seconds)\n",
      "Cbc0038I Full problem 147 rows 252 columns, reduced to 7 rows 13 columns\n",
      "Cbc0012I Integer solution of -6726 found by RINS after 0 iterations and 0 nodes (0.09 seconds)\n",
      "Cbc0012I Integer solution of -9364 found by DiveCoefficient after 433 iterations and 0 nodes (0.67 seconds)\n",
      "Cbc0031I 108 added rows had average density of 6.0555556\n",
      "Cbc0013I At root node, 108 cuts changed objective from -9978.3177 to -9374.0055 in 20 passes\n",
      "Cbc0014I Cut generator 0 (Probing) - 2522 row cuts average 3.4 elements, 0 column cuts (0 active)  in 0.070 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 1 (Gomory) - 236 row cuts average 112.5 elements, 0 column cuts (0 active)  in 0.032 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 2 (Knapsack) - 17 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.016 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 3 (Clique) - 21 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.005 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 4 (OddWheel) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.023 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 20 row cuts average 14.1 elements, 0 column cuts (0 active)  in 0.003 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 6 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.002 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 7 (TwoMirCuts) - 161 row cuts average 85.9 elements, 0 column cuts (0 active)  in 0.052 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 8 (ZeroHalf) - 50 row cuts average 5.6 elements, 0 column cuts (0 active)  in 0.164 seconds - new frequency is 1\n",
      "Cbc0011I Exiting as integer gap of 10.005526 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -9364, took 433 iterations and 0 nodes (0.67 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.53   (Wallclock seconds):       0.70\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 228 (-10136) rows, 386 (-10130) columns and 1220 (-48564) elements\n",
      "Clp1000I sum of infeasibilities 0.000298415 - average 1.30884e-06, 339 fixed columns\n",
      "Coin0506I Presolve 34 (-194) rows, 41 (-345) columns and 123 (-1097) elements\n",
      "Clp0006I 0  Obj -24233 Dual inf 128.00871 (32)\n",
      "Clp0029I End of values pass after 40 iterations\n",
      "Clp0000I Optimal - objective value -24233\n",
      "Clp0000I Optimal - objective value -24233\n",
      "Coin0511I After Postsolve, objective -24233, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -24233 Dual inf 33820.229 (161)\n",
      "Clp0014I Perturbing problem by 0.001% of 59.661605 - largest nonzero change 2.8983519e-05 ( 0.0028602104%) - largest zero change 2.9394864e-05\n",
      "Clp0000I Optimal - objective value -24233\n",
      "Clp0000I Optimal - objective value -24233\n",
      "Clp0000I Optimal - objective value -24233\n",
      "Coin0511I After Postsolve, objective -24233, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -24233 - 0 iterations time 0.022, Presolve 0.01, Idiot 0.01\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 10130 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 32 strengthened rows, 4 substitutions\n",
      "Cgl0004I processed model has 222 rows, 382 columns (382 integer (380 of which binary)) and 1204 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.250%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart solution provided values for 129 of 382 integer variables, 2 variables are still fractional.\n",
      "Cbc0038I Full problem 222 rows 382 columns, reduced to 15 rows 11 columns\n",
      "Cbc0038I Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
      "Cbc0045I Mini branch and bound defined values for remaining variables in 0.02 seconds.\n",
      "Cbc0045I MIPStart provided solution with cost 165\n",
      "Cbc0012I Integer solution of 420 found by Reduced search after 0 iterations and 0 nodes (0.18 seconds)\n",
      "Cbc0038I Full problem 222 rows 382 columns, reduced to 66 rows 111 columns\n",
      "Cbc0012I Integer solution of -13611 found by RINS after 0 iterations and 0 nodes (0.20 seconds)\n",
      "Cbc0031I 106 added rows had average density of 6.8773585\n",
      "Cbc0013I At root node, 106 cuts changed objective from -15395.79 to -14295.24 in 26 passes\n",
      "Cbc0014I Cut generator 0 (Probing) - 3339 row cuts average 3.3 elements, 2 column cuts (2 active)  in 0.076 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 1 (Gomory) - 329 row cuts average 70.9 elements, 0 column cuts (0 active)  in 0.024 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 2 (Knapsack) - 10 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.019 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 3 (Clique) - 23 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.006 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 4 (OddWheel) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.054 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 56 row cuts average 13.8 elements, 0 column cuts (0 active)  in 0.005 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 6 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.002 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 7 (TwoMirCuts) - 144 row cuts average 67.8 elements, 0 column cuts (0 active)  in 0.071 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 8 (ZeroHalf) - 25 row cuts average 6.8 elements, 0 column cuts (0 active)  in 0.161 seconds - new frequency is 1\n",
      "Cbc0011I Exiting as integer gap of 684.23953 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -13611, took 1202 iterations and 0 nodes (0.87 seconds)\n",
      "Cbc0035I Maximum depth 0, 5 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.72   (Wallclock seconds):       0.92\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 446 (-20231) rows, 794 (-20228) columns and 2505 (-98615) elements\n",
      "Clp1000I sum of infeasibilities 3.12639e-11 - average 7.00984e-14, 705 fixed columns\n",
      "Coin0506I Presolve 63 (-383) rows, 82 (-712) columns and 246 (-2259) elements\n",
      "Clp0006I 0  Obj -45997.205 Dual inf 138.70573 (68)\n",
      "Clp0029I End of values pass after 82 iterations\n",
      "Clp0000I Optimal - objective value -46022\n",
      "Clp0000I Optimal - objective value -46022\n",
      "Coin0511I After Postsolve, objective -46022, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -46022 Dual inf 55792.129 (334)\n",
      "Clp0014I Perturbing problem by 0.001% of 46.525431 - largest nonzero change 2.7949308e-05 ( 0.0027581502%) - largest zero change 2.982246e-05\n",
      "Clp0000I Optimal - objective value -46022\n",
      "Clp0000I Optimal - objective value -46022\n",
      "Clp0000I Optimal - objective value -46022\n",
      "Coin0511I After Postsolve, objective -46022, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -46022 - 0 iterations time 0.042, Presolve 0.01, Idiot 0.02\n",
      "\n",
      "Starting MIP optimization\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 20228 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 50 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 445 rows, 793 columns (793 integer (791 of which binary)) and 2502 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.121%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 633\n",
      "Cbc0012I Integer solution of 633 found by Reduced search after 0 iterations and 0 nodes (0.34 seconds)\n",
      "Cbc0038I Full problem 445 rows 793 columns, reduced to 143 rows 253 columns\n",
      "Cbc0012I Integer solution of -29634 found by RINS after 0 iterations and 0 nodes (0.37 seconds)\n",
      "Cbc0011I Exiting as integer gap of 1802.261 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -29634, took 0 iterations and 0 nodes (0.37 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.38   (Wallclock seconds):       0.44\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 833 (-44812) rows, 1726 (-44806) columns and 5772 (-220908) elements\n",
      "Clp1000I sum of infeasibilities 0.000323475 - average 3.88326e-07, 1252 fixed columns\n",
      "Coin0506I Presolve 210 (-623) rows, 393 (-1333) columns and 1401 (-4371) elements\n",
      "Clp0006I 0  Obj -84808.893 Primal inf 2.0948119e-05 (2) Dual inf 4.7799014e+10 (381)\n",
      "Clp0029I End of values pass after 393 iterations\n",
      "Clp0014I Perturbing problem by 0.001% of 142.98647 - largest nonzero change 2.9706388e-05 ( 0.0028795785%) - largest zero change 2.9589504e-05\n",
      "Clp0000I Optimal - objective value -85542\n",
      "Clp0000I Optimal - objective value -85542\n",
      "Coin0511I After Postsolve, objective -85542, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -85542 Dual inf 78738.891 (576)\n",
      "Clp0014I Perturbing problem by 0.001% of 32.298419 - largest nonzero change 2.9709032e-05 ( 0.0029318069%) - largest zero change 2.9936918e-05\n",
      "Clp0000I Optimal - objective value -85572\n",
      "Clp0000I Optimal - objective value -85572\n",
      "Clp0000I Optimal - objective value -85572\n",
      "Coin0511I After Postsolve, objective -85572, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -85572 - 0 iterations time 0.112, Presolve 0.03, Idiot 0.07\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cgl0002I 44806 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 77 strengthened rows, 2 substitutions\n",
      "Cgl0004I processed model has 829 rows, 1723 columns (1723 integer (1721 of which binary)) and 5761 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.053%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 1180\n",
      "Cbc0038I Initial state - 51 integers unsatisfied sum - 14.4113\n",
      "Cbc0038I Pass   1: suminf.    5.33378 (24) obj. -68983.8 iterations 707\n",
      "Cbc0038I Pass   2: suminf.    5.34521 (24) obj. -68983.8 iterations 1\n",
      "Cbc0038I Pass   3: suminf.    7.07124 (26) obj. -52859.7 iterations 426\n",
      "Cbc0038I Pass   4: suminf.    7.13133 (25) obj. -52812.6 iterations 75\n",
      "Cbc0038I Pass   5: suminf.    7.13133 (25) obj. -52812.6 iterations 7\n",
      "Cbc0038I Pass   6: suminf.    7.59145 (26) obj. -46072.5 iterations 321\n",
      "Cbc0038I Pass   7: suminf.    7.58408 (26) obj. -45512.6 iterations 51\n",
      "Cbc0038I Pass   8: suminf.    7.58408 (26) obj. -45512.6 iterations 5\n",
      "Cbc0038I Pass   9: suminf.    7.58408 (26) obj. -45512.6 iterations 17\n",
      "Cbc0038I Pass  10: suminf.    8.26694 (29) obj. -38681.8 iterations 349\n",
      "Cbc0038I Pass  11: suminf.    7.77845 (26) obj. -35817.2 iterations 63\n",
      "Cbc0038I Pass  12: suminf.    7.77845 (26) obj. -35817.2 iterations 2\n",
      "Cbc0038I Pass  13: suminf.   10.13862 (29) obj. -32205.2 iterations 338\n",
      "Cbc0038I Pass  14: suminf.    9.74702 (28) obj. -32225.1 iterations 57\n",
      "Cbc0038I Pass  15: suminf.    9.74702 (28) obj. -32225.1 iterations 2\n",
      "Cbc0038I Pass  16: suminf.   10.18103 (26) obj. -29353 iterations 289\n",
      "Cbc0038I Pass  17: suminf.   10.18103 (26) obj. -29353 iterations 53\n",
      "Cbc0038I Pass  18: suminf.   10.18103 (26) obj. -29353 iterations 7\n",
      "Cbc0038I Pass  19: suminf.   10.30854 (27) obj. -28382.4 iterations 315\n",
      "Cbc0038I Pass  20: suminf.   10.30854 (27) obj. -28382.4 iterations 46\n",
      "Cbc0038I Pass  21: suminf.   10.30854 (27) obj. -28382.4 iterations 9\n",
      "Cbc0038I Pass  22: suminf.    9.76418 (26) obj. -24426.8 iterations 298\n",
      "Cbc0038I Pass  23: suminf.    9.45499 (25) obj. -24427 iterations 41\n",
      "Cbc0038I Pass  24: suminf.    9.45499 (25) obj. -24427 iterations 9\n",
      "Cbc0038I Pass  25: suminf.    9.18415 (24) obj. -24716 iterations 260\n",
      "Cbc0038I Pass  26: suminf.    9.18415 (24) obj. -24716 iterations 37\n",
      "Cbc0038I Pass  27: suminf.    9.18415 (24) obj. -24716 iterations 7\n",
      "Cbc0038I Pass  28: suminf.   11.04386 (28) obj. -25898.7 iterations 334\n",
      "Cbc0038I Pass  29: suminf.    9.77662 (25) obj. -22387 iterations 46\n",
      "Cbc0038I Pass  30: suminf.    9.77662 (25) obj. -22387 iterations 1\n",
      "Cbc0038I No solution found this major pass\n",
      "Cbc0038I Before mini branch and bound, 390 integers at bound fixed and 0 continuous\n",
      "Cbc0038I Mini branch and bound did not improve solution (0.84 seconds)\n",
      "Cbc0038I After 0.84 seconds - Feasibility pump exiting - took 0.10 seconds\n",
      "Cbc0031I 559 added rows had average density of 3.3595707\n",
      "Cbc0013I At root node, 559 cuts changed objective from -69298.062 to -63762.951 in 25 passes\n",
      "Cbc0014I Cut generator 0 (Probing) - 11634 row cuts average 3.9 elements, 0 column cuts (0 active)  in 0.262 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 1 (Gomory) - 86 row cuts average 180.2 elements, 0 column cuts (0 active)  in 0.425 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 2 (Knapsack) - 6 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.061 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 3 (Clique) - 127 row cuts average 2.0 elements, 0 column cuts (0 active)  in 0.136 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 4 (OddWheel) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.908 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 51 row cuts average 41.0 elements, 0 column cuts (0 active)  in 0.021 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 6 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.008 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 7 (TwoMirCuts) - 110 row cuts average 172.9 elements, 0 column cuts (0 active)  in 0.195 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 8 (ZeroHalf) - 127 row cuts average 19.3 elements, 0 column cuts (0 active)  in 0.612 seconds - new frequency is 1\n",
      "Cbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible -63762.951 (3.97 seconds)\n",
      "Cbc0012I Integer solution of -63603 found by rounding after 2051 iterations and 2 nodes (4.14 seconds)\n",
      "Cbc0011I Exiting as integer gap of 159.95117 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -63603, took 2084 iterations and 3 nodes (4.16 seconds)\n",
      "Cbc0032I Strong branching done 32 times (694 iterations), fathomed 0 nodes and fixed 0 variables\n",
      "Cbc0035I Maximum depth 1, 272 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       4.09   (Wallclock seconds):       4.27\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 12 (-284) rows, 18 (-284) columns and 36 (-668) elements\n",
      "Clp1000I sum of infeasibilities 0 - average 0, 16 fixed columns\n",
      "Coin0506I Presolve 0 (-12) rows, 0 (-18) columns and 0 (-36) elements\n",
      "Clp0000I Optimal - objective value -7.99994\n",
      "Clp0000I Optimal - objective value -7.99994\n",
      "Coin0511I After Postsolve, objective -7.99994, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -7.99994 Dual inf 0.0025218221 (8)\n",
      "Clp0014I Perturbing problem by 0.001% of 602.15148 - largest nonzero change 0 ( 0%) - largest zero change 2.9225457e-05\n",
      "Clp0000I Optimal - objective value -7.99994\n",
      "Clp0000I Optimal - objective value -7.99994\n",
      "Clp0000I Optimal - objective value -7.99994\n",
      "Coin0511I After Postsolve, objective -7.99994, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -7.99993998 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 284 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 4 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 12 rows, 18 columns (18 integer (18 of which binary)) and 36 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 5.105%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 20\n",
      "Cbc0012I Integer solution of 20 found by Reduced search after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0012I Integer solution of 0 found by DiveCoefficient after 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0001I Search completed - best objective 0, took 0 iterations and 0 nodes (0.00 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.01   (Wallclock seconds):       0.01\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 49 (-2026) rows, 80 (-2026) columns and 160 (-5552) elements\n",
      "Clp1000I sum of infeasibilities 0.00100001 - average 2.04083e-05, 75 fixed columns\n",
      "Coin0506I Presolve 0 (-49) rows, 0 (-80) columns and 0 (-160) elements\n",
      "Clp0000I Optimal - objective value -36.99969\n",
      "Clp0000I Optimal - objective value -36.99969\n",
      "Coin0511I After Postsolve, objective -36.99969, infeasibilities - dual 0 (0), primal 0.000999 (1)\n",
      "Coin0512I Presolved model was optimal, full model needs cleaning up\n",
      "Clp0006I 0  Obj -36.99969 Primal inf 4.6234133e-06 (1) Dual inf 5.8012412e+10 (38)\n",
      "Clp0014I Perturbing problem by 0.001% of 431.98717 - largest nonzero change 0 ( 0%) - largest zero change 2.9225457e-05\n",
      "Clp0000I Optimal - objective value -36.99969\n",
      "Clp0000I Optimal - objective value -36.99969\n",
      "Clp0000I Optimal - objective value -36.99969\n",
      "Coin0511I After Postsolve, objective -36.99969, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -36.99968994 - 0 iterations time 0.002, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 2026 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 11 strengthened rows, 2 substitutions\n",
      "Cgl0004I processed model has 47 rows, 79 columns (79 integer (79 of which binary)) and 156 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 1.210%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 50\n",
      "Cbc0012I Integer solution of 50 found by Reduced search after 0 iterations and 0 nodes (0.02 seconds)\n",
      "Cbc0012I Integer solution of -1 found by DiveCoefficient after 0 iterations and 0 nodes (0.02 seconds)\n",
      "Cbc0001I Search completed - best objective -1, took 0 iterations and 0 nodes (0.02 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.02   (Wallclock seconds):       0.03\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 118 (-5300) rows, 212 (-5298) columns and 424 (-15096) elements\n",
      "Clp1000I sum of infeasibilities 7.72116e-12 - average 6.54335e-14, 204 fixed columns\n",
      "Coin0506I Presolve 0 (-118) rows, 0 (-212) columns and 0 (-424) elements\n",
      "Clp0000I Optimal - objective value -101.99906\n",
      "Clp0000I Optimal - objective value -101.99906\n",
      "Coin0511I After Postsolve, objective -101.99906, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -101.99906 Dual inf 0.032153232 (102)\n",
      "Clp0014I Perturbing problem by 0.001% of 232.42876 - largest nonzero change 0 ( 0%) - largest zero change 2.9054741e-05\n",
      "Clp0000I Optimal - objective value -101.99906\n",
      "Clp0000I Optimal - objective value -101.99906\n",
      "Clp0000I Optimal - objective value -101.99906\n",
      "Coin0511I After Postsolve, objective -101.99906, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -101.9990599 - 0 iterations time 0.012, Presolve 0.00, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cgl0002I 5296 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 12 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 118 rows, 212 columns (212 integer (212 of which binary)) and 424 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.462%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 80\n",
      "Cbc0012I Integer solution of 80 found by Reduced search after 0 iterations and 0 nodes (0.06 seconds)\n",
      "Cbc0012I Integer solution of -38 found by DiveCoefficient after 0 iterations and 0 nodes (0.06 seconds)\n",
      "Cbc0001I Search completed - best objective -38, took 0 iterations and 0 nodes (0.06 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.06   (Wallclock seconds):       0.07\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 183 (-10153) rows, 362 (-10152) columns and 759 (-29369) elements\n",
      "Clp1000I sum of infeasibilities 0.700962 - average 0.00383039, 286 fixed columns\n",
      "Coin0507I Presolve determined that the problem was infeasible with tolerance of 1e-08\n",
      "Clp0006I 0  Obj -156.99856 Primal inf 0.0039407968 (1) Dual inf 5.8030195e+10 (15)\n",
      "Clp0029I End of values pass after 76 iterations\n",
      "Clp0014I Perturbing problem by 0.001% of 221.94693 - largest nonzero change 2.9752174e-05 ( 0.0028840168%) - largest zero change 2.9054741e-05\n",
      "Clp0000I Optimal - objective value -156.99856\n",
      "Clp0000I Optimal - objective value -156.99856\n",
      "Clp0000I Optimal - objective value -156.99856\n",
      "Coin0511I After Postsolve, objective -156.99856, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -156.9985599 - 0 iterations time 0.012, Presolve 0.01, Idiot 0.00\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 10151 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 21 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 183 rows, 362 columns (362 integer (362 of which binary)) and 759 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.260%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 100\n",
      "Cbc0012I Integer solution of 100 found by Reduced search after 0 iterations and 0 nodes (0.10 seconds)\n",
      "Cbc0012I Integer solution of -64 found by DiveCoefficient after 0 iterations and 0 nodes (0.10 seconds)\n",
      "Cbc0012I Integer solution of -66 found by DiveCoefficient after 20 iterations and 0 nodes (0.12 seconds)\n",
      "Cbc0031I 5 added rows had average density of 18.2\n",
      "Cbc0013I At root node, 5 cuts changed objective from -71.373737 to -66 in 4 passes\n",
      "Cbc0014I Cut generator 0 (Probing) - 203 row cuts average 2.3 elements, 4 column cuts (4 active)  in 0.002 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 1 (Gomory) - 5 row cuts average 32.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.001 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 4 (OddWheel) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.004 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 5 row cuts average 14.8 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is 1\n",
      "Cbc0014I Cut generator 6 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100\n",
      "Cbc0014I Cut generator 7 (TwoMirCuts) - 6 row cuts average 27.3 elements, 0 column cuts (0 active)  in 0.001 seconds - new frequency is 1\n",
      "Cbc0001I Search completed - best objective -66, took 20 iterations and 0 nodes (0.12 seconds)\n",
      "Cbc0035I Maximum depth 0, 3 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.14   (Wallclock seconds):       0.16\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 412 (-20155) rows, 866 (-20154) columns and 1831 (-59209) elements\n",
      "Clp1000I sum of infeasibilities 0.395568 - average 0.000960117, 650 fixed columns\n",
      "Coin0506I Presolve 51 (-361) rows, 93 (-773) columns and 142 (-1689) elements\n",
      "Clp0006I 0  Obj -373.99631 Primal inf 0.39556698 (1) Dual inf 1.1246827e+09 (46)\n",
      "Clp0029I End of values pass after 93 iterations\n",
      "Clp0000I Optimal - objective value -373.99645\n",
      "Clp0000I Optimal - objective value -373.99645\n",
      "Coin0511I After Postsolve, objective -373.99645, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0006I 0  Obj -373.99645 Dual inf 0.087002863 (276)\n",
      "Clp0014I Perturbing problem by 0.001% of 139.23854 - largest nonzero change 2.9752174e-05 ( 0.0028840168%) - largest zero change 2.9669812e-05\n",
      "Clp0000I Optimal - objective value -373.99645\n",
      "Clp0000I Optimal - objective value -373.99645\n",
      "Clp0000I Optimal - objective value -373.99645\n",
      "Coin0511I After Postsolve, objective -373.99645, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -373.9964498 - 0 iterations time 0.022, Presolve 0.01, Idiot 0.01\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n",
      "Cgl0002I 20153 variables fixed\n",
      "Cgl0003I 0 fixed, 0 tightened bounds, 30 strengthened rows, 0 substitutions\n",
      "Cgl0004I processed model has 412 rows, 866 columns (866 integer (866 of which binary)) and 1831 elements\n",
      "Coin3009W Conflict graph built in 0.000 seconds, density: 0.108%\n",
      "Cgl0015I Clique Strengthening extended 0 cliques, 0 were dominated\n",
      "Cbc0045I MIPStart provided solution with cost 100\n",
      "Cbc0012I Integer solution of 100 found by Reduced search after 0 iterations and 0 nodes (0.22 seconds)\n",
      "Cbc0012I Integer solution of -234 found by DiveCoefficient after 0 iterations and 0 nodes (0.22 seconds)\n",
      "Cbc0011I Exiting as integer gap of 20.678207 less than 1e-10 or 10%\n",
      "Cbc0001I Search completed - best objective -234, took 0 iterations and 0 nodes (0.22 seconds)\n",
      "Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
      "Total time (CPU seconds):       0.24   (Wallclock seconds):       0.29\n",
      "\n",
      "Starting solution of the Linear programming relaxation problem using Primal Simplex\n",
      "\n",
      "Coin0506I Presolve 781 (-44837) rows, 1694 (-44836) columns and 3607 (-132953) elements\n",
      "Clp1000I sum of infeasibilities 0.00200006 - average 2.56089e-06, 1280 fixed columns\n",
      "Coin0506I Presolve 93 (-688) rows, 188 (-1506) columns and 312 (-3295) elements\n",
      "Clp0006I 0  Obj -722.99269 Dual inf 1.581903 (92)\n",
      "Clp0029I End of values pass after 188 iterations\n",
      "Clp0000I Optimal - objective value -722.99306\n",
      "Clp0000I Optimal - objective value -722.99306\n",
      "Coin0511I After Postsolve, objective -722.99306, infeasibilities - dual 0 (0), primal 0.001998 (2)\n",
      "Coin0512I Presolved model was optimal, full model needs cleaning up\n",
      "Clp0006I 0  Obj -722.99306 Primal inf 9.2468265e-06 (2) Dual inf 1.1602482e+11 (562)\n",
      "Clp0014I Perturbing problem by 0.001% of 109.66418 - largest nonzero change 2.9794711e-05 ( 0.0028881401%) - largest zero change 2.982246e-05\n",
      "Clp0000I Optimal - objective value -722.99306\n",
      "Clp0000I Optimal - objective value -722.99306\n",
      "Clp0000I Optimal - objective value -722.99306\n",
      "Coin0511I After Postsolve, objective -722.99306, infeasibilities - dual 0 (0), primal 0 (0)\n",
      "Clp0032I Optimal objective -722.9930597 - 0 iterations time 0.042, Presolve 0.03, Idiot 0.02\n",
      "\n",
      "Starting MIP optimization\n",
      "OptimizationStatus.OPTIMAL\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, problem in enumerate(problems):\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "    status = []\n",
    "    for row_idx, n in enumerate(n_points):\n",
    "\n",
    "        points, _ = make_moons(n, noise=0.15)\n",
    "        facilities = utils.generate_candidate_facilities(points, n / 50)\n",
    "        dist_matrix = distance_matrix(points, facilities)\n",
    "        facilities_to_site = round(len(facilities) / 2)\n",
    "        if i == 0:\n",
    "            p = MaximizeCoverage(\n",
    "                points,\n",
    "                facilities,\n",
    "                dist_matrix,\n",
    "                dist_cutoff=0.2,\n",
    "                facilities_to_site=facilities_to_site,\n",
    "                max_gap=max_mip_gap,\n",
    "            ).optimize(max_seconds=max_seconds)\n",
    "        elif i == 1:\n",
    "            p = MaximizeCoverageCapacitated(\n",
    "                points,\n",
    "                facilities,\n",
    "                dist_matrix,\n",
    "                capacities=utils.generate_facility_capacities(len(facilities)),\n",
    "                dist_cutoff=0.2,\n",
    "                facilities_to_site=facilities_to_site,\n",
    "                max_gap=max_mip_gap,\n",
    "            ).optimize(max_seconds=max_seconds)\n",
    "        if i == 2:\n",
    "            p = MaximizeCoverageMinimizeCost(\n",
    "                points,\n",
    "                facilities,\n",
    "                dist_matrix,\n",
    "                dist_cutoff=0.2,\n",
    "                facilities_to_site=facilities_to_site,\n",
    "                max_gap=max_mip_gap,\n",
    "            ).optimize(max_seconds=max_seconds)\n",
    "        elif i == 3:\n",
    "            p = MaximizeCoverageMinimizeFacilities(\n",
    "                points,\n",
    "                facilities,\n",
    "                dist_matrix,\n",
    "                dist_cutoff=0.2,\n",
    "                max_gap=max_mip_gap,\n",
    "            ).optimize(max_seconds=max_seconds)\n",
    "        print(p.model.status)\n",
    "\n",
    "        y.append(float(\"{:.2f}\".format(p.result.time_elapsed)))\n",
    "        status.append(p.model.status)\n",
    "\n",
    "    colors = make_colors(status)\n",
    "    x = list(range(0, len(colors)))\n",
    "    ax = plt.subplot()\n",
    "    ax.cla()\n",
    "    ax.title.set_text(problem[\"title\"])\n",
    "    ax.scatter(x=x, y=y, s=40, c=colors)\n",
    "    plt.xticks(x, [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"])\n",
    "    ax.set_xlabel(\"Experiments (maximum MIP gap = 0.1)\")\n",
    "    ax.set_ylabel(\"Elapsed time (in seconds, max = 2000s)\")\n",
    "    ax.set_xmargin(0.2)\n",
    "    for i, x_val in enumerate(x):\n",
    "        plt.annotate(\n",
    "            f\"{n_points[i]} / {int(n_points[i]/50)} / {int(n_points[i]/50/2)}\",\n",
    "            (i + 0.1, y[i]),\n",
    "            fontsize=10,\n",
    "            bbox=dict(boxstyle=\"round\", fc=(1.0, 1.0, 1.0), ec=\"black\"),\n",
    "        )\n",
    "    ax.legend(\n",
    "        custom_markers,\n",
    "        [\"Optimal\", \"Feasible\", \"Infeasible\", \"No solution found\"],\n",
    "        loc=\"upper left\",\n",
    "        facecolor=\"white\",\n",
    "        framealpha=1,\n",
    "    )\n",
    "    plt.show()\n",
    "    #plt.savefig(problem[\"filename\"], dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e5c4b6b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "location-allocation-ss8QIqgb-py3.8",
   "language": "python",
   "name": "location-allocation-ss8qiqgb-py3.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}