vrp_comparison_results.ipynb

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   "source": [
    "import numpy as np\n",
    "from utils import compare_solvers"
   ]
  },
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    "class Initializer:\n",
    "\n",
    "    def __init__(self, n, a, b):\n",
    "        self.n = n\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "\n",
    "    def generate_nodes_and_weight_matrix(self):\n",
    "\n",
    "        n = self.n\n",
    "        a = self.a\n",
    "        b = self.b\n",
    "        \n",
    "        np.random.seed(100*a + b)\n",
    "\n",
    "        x = (np.random.rand(n) - 0.5) * 50\n",
    "        y = (np.random.rand(n) - 0.5) * 50\n",
    "\n",
    "        weight_matrix = np.zeros([n, n])\n",
    "        for i in range(n):\n",
    "            for j in range(i+1, n):\n",
    "                weight_matrix[i, j] = (x[i] - x[j]) ** 2 + (y[i] - y[j]) ** 2\n",
    "                weight_matrix[j, i] = weight_matrix[i, j]\n",
    "\n",
    "        return x, y, weight_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "002a0699",
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   "outputs": [],
   "source": [
    "### Select the type of model to solve VRP\n",
    "#    1: Constrained Quadratic Model - A new model released by D-Wave Systems capable of encoding Quadratically Constrained Quadratic Programs (QCQPs)\n",
    "#    2: Binary Quadratic Model - A model that encodes Ising or QUBO problems\n",
    "model = 'CQM'\n",
    "\n",
    "\n",
    "### The time limit (in seconds) for the solvers to run on the `LeapHybridCQMSampler` backend\n",
    "time_limit = 5\n",
    "\n",
    "\n",
    "### Select solver\n",
    "#    1: RAS (Route Activation Solver)\n",
    "#    2: FQS (Full QUBO Solver)\n",
    "#    3: GPS (Guillermo, Parfait, Saúl) (only using CQM)\n",
    "#    4: DBSCANS (Density-Based Spatial Clustering of Applications with Noise - Solver)\n",
    "#    5: SPS (Solution Partition Solver)\n",
    "solver = 'ras'\n",
    "\n",
    "# Number of iterations to get the average approximation ratio for a particular solver\n",
    "# Warning! More iterations will take more time and resources to run\n",
    "n_iter = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2a2505f-c24e-4ccd-a86f-9c66b3589b42",
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   "source": [
    "for n in range(10, 13):                                ### Here, (2, 6) could be replaced with the some other range of no. of locations you want.\n",
    "    for instance in range(4):                         ### Here, (10) could be replaced with some other number of instcnces you want to generate for a particular no. of locations.\n",
    "        initializer = Initializer(n, n, instance)\n",
    "        xc, yc, cost = initializer.generate_nodes_and_weight_matrix()\n",
    "        for m in range(1, n):\n",
    "            comparison_table = compare_solvers(n-1, m, cost, xc, yc, n_iter=n_iter, time_limit=time_limit)\n",
    "            print(f'\\nn = {n}, m = {m}, instance = {instance}')\n",
    "            print('Classical cost from best known solution:', comparison_table[0]['exact_min_cost'])\n",
    "            for solver_id in comparison_table[1]:\n",
    "                print(f'{solver_id}:', '\\t', f'quantum cost = {comparison_table[1][solver_id][\"avg_min_cost\"]}',\n",
    "                                       '\\t', f'runtime = {comparison_table[1][solver_id][\"avg_runtime\"]}',\n",
    "                                       '\\t', f'number of variables = {comparison_table[1][solver_id][\"num_vars\"]}',\n",
    "                                       '\\t', f'approximation ratio = {comparison_table[1][solver_id][\"approximation_ratio\"]}'\n",
    "                )"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "f2a2505f-c24e-4ccd-a86f-9c66b3589b42",
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   "source": [
    "# for n in range(2, 6):                               ### Here, (2, 6) could be replaced with the some other range of no. of locations you want.\n",
    "#     for instance in range(10):                      ### Here, (10) could be replaced with some other number of instcnces you want to generate for a particular no. of locations.\n",
    "#         initializer = Initializer(n, n, instance)\n",
    "#         xc, yc, cost = initializer.generate_nodes_and_weight_matrix()\n",
    "#         for m in range(1, n):\n",
    "#             comparison_table = compare_solvers(n-1, m, cost, xc, yc, n_iter=n_iter, time_limit=time_limit)\n",
    "#             print(f'n = {n}, m = {m}, instance = {instance}')\n",
    "#             print('Classical cost from best known solution:', comparison_table[0]['exact_min_cost'])\n",
    "#             for solver_id in comparison_table[1]:\n",
    "#                 print(f'{solver_id}:', '\\t', f'quantum cost = {comparison_table[1][solver_id][\"avg_min_cost\"]}',\n",
    "#                                     '\\t', f'runtime = {comparison_table[1][solver_id][\"avg_runtime\"]}',\n",
    "#                                     '\\t', f'number of variables = {comparison_table[1][solver_id][\"num_vars\"]}',\n",
    "#                                     '\\t', f'approximation ratio = {comparison_table[1][solver_id][\"approximation_ratio\"]}'\n",
    "#                 )"
   ]
  }
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