TSP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a5d8919-f92c-4602-b1a0-766f721cf8db",
   "metadata": {},
   "source": [
    "# Solving the TSP\n",
    "\n",
    "In order to solve the TSP on a graph $G=(V, E)$ we augment the 2-matching formulation introduced previously to include *connectivity constraints*\n",
    "$$\n",
    "x(\\delta(S)) \\geq 2 \\quad \\forall \\: \\emptyset \\neq S \\subsetneq V.\n",
    "$$\n",
    "Since there is an exponential number of these constraints, we *separate* them using a constraint handler.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "wired-cricket",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyscipopt\n",
    "from pyscipopt import Model, quicksum\n",
    "\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = (20.0, 20.0)\n",
    "matplotlib.rcParams['axes.titlesize'] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "looking-tracker",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 40\n",
    "\n",
    "np.random.seed(42)\n",
    "coordinates = np.random.random((n, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "certified-diesel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe608766be0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(coordinates[:, 0], coordinates[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "express-carnival",
   "metadata": {},
   "source": [
    "# 2-Matching formulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "demonstrated-sound",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vertices(n):\n",
    "    return range(n)\n",
    "\n",
    "def edges(n):\n",
    "    return [(i, j) for i in vertices(n) for j in vertices(n) if i < j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "protective-galaxy",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.DiGraph(edges(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "powered-johns",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(coordinates, i, j):\n",
    "    dx = coordinates[i, 0] - coordinates[j, 0]\n",
    "    dy = coordinates[i, 1] - coordinates[j, 1]\n",
    "    return np.hypot(dx, dy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "continued-uzbekistan",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_variables(model, graph, coordinates):\n",
    "    return {edge:model.addVar(name=f\"x_{edge}\", vtype=\"B\", obj=dist(coordinates, *edge)) \n",
    "            for edge in graph.edges}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "second-living",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_constraints(model, graph, variables, **props):\n",
    "    cons = {}\n",
    "    for v in graph:\n",
    "        cons[v] = model.addCons(quicksum((variables[edge] for edge in graph.out_edges([v]))) +\n",
    "                                quicksum((variables[edge] for edge in graph.in_edges([v]))) == 2, \n",
    "                                **props)\n",
    "    \n",
    "    return cons\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "sufficient-pottery",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model()\n",
    "model.redirectOutput()\n",
    "\n",
    "variables  = create_variables(model, G, coordinates)\n",
    "\n",
    "_ = create_constraints(model, G, variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "involved-peter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "presolving:\n",
      "   (0.0s) running MILP presolver\n",
      "   (0.0s) MILP presolver found nothing\n",
      "   (0.0s) probing: 51/780 (6.5%) - 0 fixings, 0 aggregations, 0 implications, 0 bound changes\n",
      "   (0.0s) probing aborted: 50/50 successive totally useless probings\n",
      "   (0.0s) symmetry computation started: requiring (bin +, int -, cont +), (fixed: bin -, int +, cont -)\n",
      "   (0.0s) no symmetry present\n",
      "presolving (1 rounds: 1 fast, 1 medium, 1 exhaustive):\n",
      " 0 deleted vars, 0 deleted constraints, 0 added constraints, 0 tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients\n",
      " 0 implications, 0 cliques\n",
      "presolved problem has 780 variables (780 bin, 0 int, 0 impl, 0 cont) and 40 constraints\n",
      "     40 constraints of type <linear>\n",
      "Presolving Time: 0.03\n",
      "\n",
      " time | node  | left  |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr|  dualbound   | primalbound  |  gap   | compl. \n",
      "p 0.1s|     1 |     0 |     0 |     - |   locks|   0 | 780 |  50 |  40 |   0 |  0 |  10 |   0 | 0.000000e+00 | 1.988062e+01 |    Inf | unknown\n",
      "  0.1s|     1 |     0 |    64 |     - |  9812k |   0 | 780 |  50 |  40 |   0 |  0 |  10 |   0 | 4.339424e+00 | 1.988062e+01 | 358.14%| unknown\n",
      "  0.1s|     1 |     0 |    68 |     - |    10M |   0 | 780 |  50 |  42 |   2 |  1 |  10 |   0 | 4.370986e+00 | 1.988062e+01 | 354.83%| unknown\n",
      "r 0.1s|     1 |     0 |    68 |     - |shifting|   0 | 780 |  50 |  42 |   2 |  1 |  10 |   0 | 4.370986e+00 | 4.436078e+00 |   1.49%| unknown\n",
      "  0.1s|     1 |     0 |    71 |     - |    11M |   0 | 780 |  50 |  44 |   4 |  2 |  10 |   0 | 4.394878e+00 | 4.436078e+00 |   0.94%| unknown\n",
      "  0.1s|     1 |     0 |    71 |     - |    11M |   0 | 780 |  50 |  44 |   4 |  2 |  10 |   0 | 4.394878e+00 | 4.436078e+00 |   0.94%| unknown\n",
      "  0.1s|     1 |     0 |    74 |     - |    11M |   0 | 780 |  50 |  50 |  10 |  3 |  10 |   0 | 4.402221e+00 | 4.436078e+00 |   0.77%| unknown\n",
      "  0.1s|     1 |     0 |    74 |     - |    11M |   0 | 780 |  50 |  50 |  10 |  3 |  10 |   0 | 4.402221e+00 | 4.436078e+00 |   0.77%| unknown\n",
      "  0.1s|     1 |     0 |    74 |     - |    11M |   0 | 780 |  39 |  50 |  10 |  5 |  10 |   0 | 4.402221e+00 | 4.436078e+00 |   0.77%| unknown\n",
      "d 0.1s|     1 |     0 |    74 |     - |farkasdi|   0 | 780 |  39 |  50 |   0 |  7 |  10 |   0 | 4.402221e+00 | 4.402221e+00 |   0.00%| unknown\n",
      "  0.1s|     1 |     0 |    74 |     - |    11M |   0 | 780 |  39 |  50 |  10 |  7 |  10 |   0 | 4.402221e+00 | 4.402221e+00 |   0.00%| unknown\n",
      "\n",
      "SCIP Status        : problem is solved [optimal solution found]\n",
      "Solving Time (sec) : 0.13\n",
      "Solving Nodes      : 1\n",
      "Primal Bound       : +4.40222057633342e+00 (4 solutions)\n",
      "Dual Bound         : +4.40222057633342e+00\n",
      "Gap                : 0.00 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'optimal'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.optimize()\n",
    "\n",
    "model.getStatus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "compact-connection",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = [edge for (edge, variable) in variables.items() if round(model.getVal(variable)) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "advanced-invalid",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sol(sol, coordinates):\n",
    "    x = coordinates[:,0]\n",
    "    y = coordinates[:,1]\n",
    "    lines = [[(x[i], y[i]), (x[j], y[j])] for (i, j) in solution]\n",
    "\n",
    "    plt.scatter(x, y)\n",
    "\n",
    "    lc = matplotlib.collections.LineCollection(lines, linewidths=2)\n",
    "\n",
    "    ax = plt.gca()\n",
    "\n",
    "    ax.add_collection(lc)\n",
    "    ax.autoscale()\n",
    "    \n",
    "    obj = sum(dist(coordinates, *edge) for edge in sol)\n",
    "    plt.title(f\"A solution with objective {obj}\")\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "tough-accommodation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sol(solution, coordinates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "based-fireplace",
   "metadata": {},
   "source": [
    "# Adding a custom constraint handler for subtour elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "revolutionary-murder",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConnectivityConshdlr(pyscipopt.scip.Conshdlr):\n",
    "    \n",
    "    def __init__(self, model, graph, variables):\n",
    "        self.model = model\n",
    "        self.graph = graph\n",
    "        self.variables = variables\n",
    "    \n",
    "    def conslock(self, constraint, locktype, nlockspos, nlocksneg):\n",
    "        '''variable rounding lock method of constraint handler'''\n",
    "\n",
    "        for (edge, variable) in self.variables.items():\n",
    "            self.model.addVarLocks(variable, nlockspos, nlocksneg)\n",
    "\n",
    "        return {}\n",
    "    \n",
    "    def consenfolp(self, constraints, nusefulconss, solinfeasible):\n",
    "        '''calls enforcing method of constraint handler for LP solution for all constraints added'''\n",
    "        \n",
    "        (v, p) = self.min_cut(any_cut=False)\n",
    "        \n",
    "        row = self.model.createEmptyRowUnspec(lhs = 2., rhs = np.inf)\n",
    "        \n",
    "        if p is None:\n",
    "            return {\"result\": pyscipopt.SCIP_RESULT.FEASIBLE}\n",
    "        \n",
    "        (reachable, non_reachable) = p\n",
    "        cutset = set()\n",
    "        \n",
    "        for u, nbrs in ((n, G[n]) for n in reachable):\n",
    "            cutset.update((u, v) for v in nbrs if v in non_reachable)\n",
    "            \n",
    "        for u, nbrs in ((n, G[n]) for n in non_reachable):\n",
    "            cutset.update((u, v) for v in nbrs if v in reachable)\n",
    "            \n",
    "        \n",
    "        model.addCons(quicksum(self.variables[edge] for edge in cutset) >= 1)\n",
    "        return {}\n",
    "\n",
    "    def consenfops(self, constraints, nusefulconss, solinfeasible, objinfeasible):\n",
    "        '''calls enforcing method of constraint handler for pseudo solution for all constraints added'''\n",
    "        \n",
    "        (v, p) = self.min_cut(any_cut=True)\n",
    "        \n",
    "        result = pyscipopt.SCIP_RESULT.FEASIBLE if p is None else pyscipopt.SCIP_RESULT.INFEASIBLE    \n",
    "\n",
    "        return {\"result\": result}\n",
    "\n",
    "    def conscheck(self, constraints, solution, checkintegrality, checklprows, printreason, completely):\n",
    "        '''calls feasibility check method of constraint handler '''\n",
    "        \n",
    "        (v, p) = self.min_cut(any_cut=True)\n",
    "        \n",
    "        result = pyscipopt.SCIP_RESULT.FEASIBLE if p is None else pyscipopt.SCIP_RESULT.INFEASIBLE    \n",
    "\n",
    "        return {\"result\": result}\n",
    "    \n",
    "    def min_cut(self, any_cut):\n",
    "        \n",
    "        nx.set_edge_attributes(self.graph, self.fractional_values(), \"capacity\")\n",
    "        \n",
    "        is_feasible = True\n",
    "        \n",
    "        v_min = np.inf\n",
    "        p_min = None\n",
    "        \n",
    "        for s in self.graph:\n",
    "            for t in self.graph:\n",
    "                if t <= s:\n",
    "                    continue\n",
    "                    \n",
    "                (v, p) = nx.minimum_cut(self.graph.to_undirected(), s, t)\n",
    "                \n",
    "                if self.model.isGE(v, 2.):\n",
    "                    continue\n",
    "                \n",
    "                if any_cut: \n",
    "                    return (v, p)\n",
    "                \n",
    "                if self.model.isLT(v, v_min):\n",
    "                    (v_min, p_min) = (v, p)\n",
    "        \n",
    "        return (v_min, p_min)\n",
    "        \n",
    "    \n",
    "    def fractional_values(self):\n",
    "        return  {edge: model.getSolVal(None, variable) for (edge, variable) in self.variables.items()}\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "romance-gathering",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model()\n",
    "model.redirectOutput()\n",
    "\n",
    "variables  = create_variables(model, G, coordinates)\n",
    "\n",
    "_ = create_constraints(model, G, variables)\n",
    "\n",
    "conshdlr = ConnectivityConshdlr(model, G, variables)\n",
    "\n",
    "\n",
    "model.includeConshdlr(conshdlr, \"Connectivity\", \"Enforces connectivity constraints\",\n",
    "                      sepapriority = 1, enfopriority = 1, chckpriority = 1, sepafreq = 10, propfreq = 50,\n",
    "                      eagerfreq = 1, maxprerounds = -1, delaysepa = False, delayprop = False, needscons = False,\n",
    "                      presoltiming = pyscipopt.SCIP_PRESOLTIMING.FAST, proptiming = pyscipopt.SCIP_PROPTIMING.BEFORELP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "prescribed-graduation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "presolving:\n",
      "   (0.0s) probing: 51/780 (6.5%) - 0 fixings, 0 aggregations, 0 implications, 0 bound changes\n",
      "   (0.0s) probing aborted: 50/50 successive totally useless probings\n",
      "   (0.0s) symmetry computation started: requiring (bin +, int -, cont +), (fixed: bin -, int +, cont -)\n",
      "   (0.0s) no symmetry present\n",
      "presolving (1 rounds: 1 fast, 1 medium, 1 exhaustive):\n",
      " 0 deleted vars, 0 deleted constraints, 0 added constraints, 0 tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients\n",
      " 0 implications, 0 cliques\n",
      "presolved problem has 780 variables (780 bin, 0 int, 0 impl, 0 cont) and 40 constraints\n",
      "     40 constraints of type <linear>\n",
      "Presolving Time: 0.03\n",
      "\n",
      " time | node  | left  |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr|  dualbound   | primalbound  |  gap   | compl. \n",
      "  0.1s|     1 |     0 |    64 |     - |  7266k |   0 | 780 |  50 |  40 |   0 |  0 |  10 |   0 | 4.339424e+00 |      --      |    Inf | unknown\n",
      "  0.3s|     1 |     0 |    89 |     - |  8346k |   0 | 780 |  50 |  42 |   2 |  1 |  10 |   0 | 4.370986e+00 |      --      |    Inf | unknown\n",
      "  0.3s|     1 |     0 |    92 |     - |  8430k |   0 | 780 |  50 |  44 |   4 |  2 |  10 |   0 | 4.394878e+00 |      --      |    Inf | unknown\n",
      "  0.3s|     1 |     0 |    96 |     - |  8435k |   0 | 780 |  50 |  47 |   7 |  3 |  10 |   0 | 4.402221e+00 |      --      |    Inf | unknown\n",
      "  4.4s|     1 |     0 |    98 |     - |  8477k |   0 | 780 |  51 |  48 |   8 |  5 |  10 |   0 | 4.407906e+00 |      --      |    Inf | unknown\n",
      "  4.5s|     1 |     0 |    99 |     - |  8497k |   0 | 780 |  51 |  50 |  10 |  6 |  10 |   0 | 4.413591e+00 |      --      |    Inf | unknown\n",
      "  8.9s|     1 |     0 |   100 |     - |  8501k |   0 | 780 |  52 |  51 |  11 |  7 |  10 |   0 | 4.414415e+00 |      --      |    Inf | unknown\n",
      "  8.9s|     1 |     0 |   104 |     - |  8708k |   0 | 780 |  52 |  53 |  13 |  8 |  10 |   0 | 4.433719e+00 |      --      |    Inf | unknown\n",
      " 13.3s|     1 |     2 |   104 |     - |  8713k |   0 | 780 |  53 |  53 |  13 |  9 |  10 |  10 | 4.436078e+00 |      --      |    Inf | unknown\n",
      "o 247s|    37 |    38 |   986 |  24.5 |rootsold|   9 | 780 |  96 |  86 |  13 |  1 |  17 |  85 | 4.565048e+00 | 6.772784e+00 |  48.36%| unknown\n",
      "* 350s|    55 |    49 |  1108 |  18.6 |    LP  |  10 | 780 | 113 | 103 |  21 |  3 |  17 |  94 | 4.620245e+00 | 4.978824e+00 |   7.76%|   0.71%\n",
      "* 416s|    67 |    56 |  1287 |  17.9 |strongbr|  10 | 780 | 144 |  85 |  21 |  3 |  42 | 100 | 4.620245e+00 | 4.932791e+00 |   6.76%|   3.06%\n",
      "  678s|   100 |    71 |  1825 |  17.4 |    19M |  10 | 780 | 223 | 134 |  27 |  2 |  91 | 120 | 4.692247e+00 | 4.932791e+00 |   5.13%|  17.44%\n",
      "* 689s|   103 |    50 |  1844 |  17.1 |    LP  |  10 | 780 | 227 | 103 |  27 |  2 |  94 | 121 | 4.692247e+00 | 4.897316e+00 |   4.37%|  22.84%\n",
      "\n",
      "SCIP Status        : problem is solved [optimal solution found]\n",
      "Solving Time (sec) : 813.35\n",
      "Solving Nodes      : 189\n",
      "Primal Bound       : +4.89731637498779e+00 (4 solutions)\n",
      "Dual Bound         : +4.89731637498779e+00\n",
      "Gap                : 0.00 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'optimal'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.optimize()\n",
    "\n",
    "model.getStatus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "paperback-courtesy",
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = [edge for (edge, variable) in variables.items() if round(model.getVal(variable)) == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "spread-sense",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sol(solution, coordinates)"
   ]
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