tutorials/demo_03_tsp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Copyright (C) 2023 Intel Corporation*<br>\n",
    "*SPDX-License-Identifier: BSD-3-Clause*<br>\n",
    "*See: https://spdx.org/licenses/*\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solving a travelling salesman problem using the Lava QUBO solver\n",
    "This notebook demonstrates the usage of a Lava-Optimization QUBO solver to solve a travelling salesman problem.\n",
    "\n",
    "Currently, the solver finds a route connecting way-points without taking the salesman's starting position into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from lava.lib.optimization.apps.tsp.problems import TravellingSalesmanProblem\n",
    "from lava.lib.optimization.apps.tsp.solver import TSPConfig, TSPSolver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_coords = [(1, 1), (2, 1), (16, 1), (16, 15), (2, 15)]\n",
    "center_coords = [(1, 1)]\n",
    "point_coords = [(2, 1), (16, 1), (16, 15), (2, 15)]\n",
    "tsp_instance = TravellingSalesmanProblem(\n",
    "    waypt_coords=point_coords, starting_pt=center_coords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualise the problem\n",
    "\n",
    "- Blue circle: starting position\n",
    "- Red triangles: way-points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tsp_instance.nodes[1][0][0], tsp_instance.nodes[1][0][1], s=25, c='b', marker='o')\n",
    "plt.scatter(np.array(tsp_instance.waypt_coords)[:, 0], np.array(tsp_instance.waypt_coords)[:, 1], s=25, c='r', marker='^')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:DRV:  SLURM is being run in background\n",
      "INFO:DRV:  Connecting to 10.54.73.26:37679\n",
      "INFO:DRV:      Host server up..............Done 0.39s\n",
      "INFO:DRV:      Mapping chipIds.............Done 0.01ms\n",
      "INFO:DRV:      Mapping coreIds.............Done 0.05ms\n",
      "INFO:DRV:      Partitioning neuron groups..Done 0.67ms\n",
      "INFO:DRV:      Mapping axons...............Done 0.21ms\n",
      "INFO:DRV:      Configuring Spike Block.....Done 0.00ms\n",
      "INFO:DRV:      Writes SpikeIO Config to FileDone 0.01ms\n",
      "INFO:DRV:      Initializes Python MQ.......Done 0.01ms\n",
      "INFO:DRV:      Partitioning MPDS...........Done 0.46ms\n",
      "INFO:DRV:      Creating Embedded Snips and ChannelsDone 5.86ms\n",
      "INFO:DRV:      Compiling Embedded snips....Done 0.80s\n",
      "INFO:DRV:      Compiling Host snips........Done 0.22ms\n",
      "INFO:DRV:      Compiling Register Probes...Done 0.35ms\n",
      "INFO:DRV:      Compiling Spike Probes......Done 0.04ms\n",
      "INFO:HST:  Args chip=0 cpu=0 /home/sumedhrr/frameworks.ai.nx.nxsdk/nxcore/arch/base/pre_execution/../../../../temp/ecc88876-6956-11ee-bb08-19f77971418b/launcher_chip0_cpu0.bin --chips=1 --remote-relay=0 \n",
      "INFO:HST:  Args chip=0 cpu=1 /home/sumedhrr/frameworks.ai.nx.nxsdk/nxcore/arch/base/pre_execution/../../../../temp/ecc88876-6956-11ee-bb08-19f77971418b/launcher_chip0_cpu1.bin --chips=1 --remote-relay=0 \n",
      "INFO:HST:  Nx...\n",
      "INFO:DRV:      Booting up..................Done 0.65s\n",
      "INFO:DRV:      Encoding probes.............Done 0.01ms\n",
      "INFO:DRV:      Transferring probes.........Done 3.60ms\n",
      "INFO:DRV:      Configuring registers.......Done 0.05s\n",
      "INFO:DRV:      Transferring spikes.........Done 0.01ms\n",
      "INFO:HST:  chip=0 msg=00018114 00ffff00 \n",
      "INFO:DRV:      Executing...................Done 4.17ms\n",
      "INFO:DRV:      Processing timeseries.......Done 0.05ms\n",
      "INFO:DRV:  Executor: 1000 timesteps........Done 0.08s\n",
      "INFO:HST:  Execution has not started yet or has finished.\n",
      "INFO:HST:  Stopping Execution : at 1000\n",
      "INFO:HST:  chip=0 cpu=1 halted, status=0x0\n",
      "INFO:HST:  chip=0 cpu=0 halted, status=0x0\n"
     ]
    }
   ],
   "source": [
    "solver = TSPSolver(tsp=tsp_instance)\n",
    "scfg = TSPConfig(backend=\"Loihi2\",\n",
    "                 hyperparameters={},\n",
    "                 target_cost=-1000000,\n",
    "                 timeout=1000,\n",
    "                 probe_time=False,\n",
    "                 log_level=20) # Change log level to 40 for suppressing the verbose output below\n",
    "np.random.seed(0)\n",
    "solver.solve(scfg=scfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDs:\n",
      "[4, 3, 2, 5]\n",
      "Coords:\n",
      "[(16, 15), (16, 1), (2, 1), (2, 15)]\n"
     ]
    }
   ],
   "source": [
    "print(f\"IDs:\\n{solver.solution.solution_path_ids}\")\n",
    "print(f\"Coords:\\n{solver.solution.solution_path_coords}\")"
   ]
  }
 ],
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