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TSP-Guidance/Investigating TSP.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h1><center>Guide Notebook for the 380CT Assignment on TSP</center></h1>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "_Kamal Bentahar_\n", | |
| "\n", | |
| "[https://github.coventry.ac.uk/380CT-1920JANMAY/TSP-Guidance](https://github.coventry.ac.uk/380CT-1920JANMAY/TSP-Guidance)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Notation and definitions" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let $G$ be a complete weighted graph with $n$ vertices...\n", | |
| "\n", | |
| "- **Complete**: the graph is undirected, has no self-loops, and each node is connected to all the other vertices.\n", | |
| "- **Weighted**: the edges have a weight (a positive integer).\n", | |
| "- **Cycle**: a path that visits every vertex once, and goes back to the start point.\n", | |
| "- **Total cost of the cycle**: sum of the edge weights of the cycle." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Definition of the problem" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Given $G$ as above, the versions of the TSP are defined as follows:\n", | |
| "\n", | |
| "* **Decisional TSP (D-TSP)**:\n", | |
| "> Given a total cost $k$, decide if $G$ is has a cycle of length $\\leq k$.\n", | |
| "\n", | |
| " **NP-complete**, because D-TSP $\\in$ NP and D-TSP $\\leq_p$ HAMCYCLE.\n", | |
| " \n", | |
| " * D-TSP $\\in$ NP: once a cycle is given (a certificate) we can quickly evaluate the its cost in $O(n)$ time to verify it is equal to $k$.\n", | |
| " * D-TSP $\\leq_p$ HAMCYCLE: Reduction from HAMCYCLE (Hoos and Stutzler, p.25).\n", | |
| "\n", | |
| "* **Search TSP**:\n", | |
| "> Given a total cost $k$, search for a cycle of length $\\leq k$ in $G$.\n", | |
| "> (If found then return it, otherwise say that there is no such cycle.)\n", | |
| " \n", | |
| "* **Optimization TSP**:\n", | |
| "> Given $G$, find a cycle of minimal total cost.\n", | |
| "\n", | |
| " **NP-Hard**, because the optimization version of (decision) NP-complete problems are automatically NP-Hard. (using the same method sketched above for **Search TSP**)\n", | |
| "\n", | |
| "The facts about the complexity classes memberships can also be found in (Garey and Johnson, 1979) and (Hoos and Stutzler, 2005)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Testing methodology" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* **Exact methods**:\n", | |
| " Average time for instances with increasing $n$.\n", | |
| "\n", | |
| "* **Greedy and meta-heuristics**:\n", | |
| " Average \"quality\" as $n$ increases.\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Random instances sampling strategy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "General TSP instances will be generated by creating symmetric adjacency matrices uniformly at random." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Code" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "First start by importing relevant libraries." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from random import randint, choice\n", | |
| "from pprint import pprint\n", | |
| "from itertools import permutations\n", | |
| "from math import inf as oo # Infinity (∞) is larger than any number\n", | |
| "from math import sqrt\n", | |
| "from time import time\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import copy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Basics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let the set of vertices be $\\{0, 1, 2,\\ldots, n-1\\}$.\n", | |
| "\n", | |
| "Without loss of generality, we can consider $0$ to be the start and end point of cycles." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "MAX_DISTANCE = 100\n", | |
| "\n", | |
| "def random_symmetric_graph(n):\n", | |
| " ''' Symmetric adjacency matrix of size nxn '''\n", | |
| " dist_matrix = [[oo for _ in range(n)] for _ in range(n)]\n", | |
| " for i in range(n):\n", | |
| " for j in range(i+1,n):\n", | |
| " v = randint(1,MAX_DISTANCE)\n", | |
| " dist_matrix[i][j] = v\n", | |
| " dist_matrix[j][i] = v\n", | |
| " return dist_matrix\n", | |
| "\n", | |
| "def random_euclidean_graph(n):\n", | |
| " ''' Symmetric adjacency matrix of a Euclidean graph of size nxn '''\n", | |
| " dist_matrix = [[oo for _ in range(n)] for _ in range(n)]\n", | |
| " points = []\n", | |
| " for p in range(n):\n", | |
| " x,y = randint(0,MAX_DISTANCE), randint(0,MAX_DISTANCE)\n", | |
| " points.append((x,y))\n", | |
| " for i in range(n):\n", | |
| " p1 = points[i]\n", | |
| " for j in range(i+1,n):\n", | |
| " p2 = points[j]\n", | |
| " distance = sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)\n", | |
| " dist_matrix[i][j] = distance\n", | |
| " dist_matrix[j][i] = distance\n", | |
| " return dist_matrix\n", | |
| "\n", | |
| "def show(G):\n", | |
| " ''' Show adjacency matrix. Useful for debugging. '''\n", | |
| " n = len(G)\n", | |
| " r = \" \"\n", | |
| " for i in range(n):\n", | |
| " r += f'{i:4}'\n", | |
| " r += '\\n -'+'-'*(4*n)+'\\n'\n", | |
| " for i in range(n):\n", | |
| " r += f'{i:2} | '\n", | |
| " for j in range(n):\n", | |
| " r += f'{G[i][j]:4}'\n", | |
| " r += '\\n'\n", | |
| " r = r.replace('inf', ' ∞')\n", | |
| " print(r)\n", | |
| "\n", | |
| "def cost(G, cycle):\n", | |
| " ''' Calculate the cost of the given cycle '''\n", | |
| " c = 0\n", | |
| " n = len(G)\n", | |
| " for i in range(n):\n", | |
| " a = cycle[i]\n", | |
| " b = cycle[(i+1)%n]\n", | |
| " c += G[a][b]\n", | |
| " return c" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " 0 1 2 3 4\n", | |
| " ---------------------\n", | |
| " 0 | ∞63.19810123729984524.04163056034261545.011109739707630.0\n", | |
| " 1 | 63.198101237299845 ∞50.9901951359278518.97366596101027653.53503525729669\n", | |
| " 2 | 24.04163056034261550.99019513592785 ∞32.24903099319427.0710678118654755\n", | |
| " 3 | 45.011109739707618.97366596101027632.2490309931942 ∞35.4682957019364\n", | |
| " 4 | 30.053.535035257296697.071067811865475535.4682957019364 ∞\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "G=random_euclidean_graph(5)\n", | |
| "show(G)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Example" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " 0 1 2 3 4\n", | |
| " ---------------------\n", | |
| " 0 | ∞ 12 19 68 74\n", | |
| " 1 | 12 ∞ 91 29 6\n", | |
| " 2 | 19 91 ∞ 50 55\n", | |
| " 3 | 68 29 50 ∞ 88\n", | |
| " 4 | 74 6 55 88 ∞\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "G=random_symmetric_graph(5)\n", | |
| "show(G)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Solution methods" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Exact methods - Exhaustive search" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The idea is to:\n", | |
| "- Consider vertex $0$ as the start and end point.\n", | |
| "- Iterate over all permutations of the vertices $\\{1,2,\\ldots, n-1\\}$.\n", | |
| " - Calculate cost of each permutation and keep track of minimum cost permutation.\n", | |
| "- Return the cycle with minimum cost.\n", | |
| "\n", | |
| "More formally, the pseudo-code is as follows:\n", | |
| "\n", | |
| "**Input**: $G$.\n", | |
| "\n", | |
| "**Output**: a cycle in $G$ of shortest cost." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "1. $bestcycle\\gets \\emptyset$\n", | |
| "2. $bestcost\\gets \\infty$\n", | |
| "3. **for all** possible cycles $p$ in $G$ (starting and ending at $0$) **do**\n", | |
| "4. $\\quad$ $c\\gets$ cost of $p$\n", | |
| "2. $\\quad$ **if** $c<bestcost$ **then**\n", | |
| "3. $\\qquad$ $bestcycle\\gets p$\n", | |
| "3. $\\qquad$ $bestcost\\gets c$\n", | |
| "4. $\\quad$ **end if**\n", | |
| "5. **end for**\n", | |
| "6. **return** $bestcycle, bestcost$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "There are $(n-1)!$ possible cycles, and each computation of a cycle's cost costs $O(n)$. So this algorithm costs $$O((n-1)!\\cdot n)=O(n!).$$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def exhaustive_search(G):\n", | |
| " n = len(G) \n", | |
| " best_cost = oo # infinity\n", | |
| " best_cycle = []\n", | |
| " for cycle in permutations(range(1,n)): # permutations of [1,2,...,n-1]\n", | |
| " cycle=[0]+list(cycle) # add the starting city: 0\n", | |
| " c = cost(G, cycle)\n", | |
| " if c < best_cost:\n", | |
| " best_cost = c\n", | |
| " best_cycle = cycle\n", | |
| " return (best_cycle, best_cost)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Testing exhaustive search time cost" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Test completely random graphs:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "8\t0.02001166343688965\n", | |
| "9\t0.12252664566040039\n", | |
| "10\t1.4062316417694092\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "pnts_n = []\n", | |
| "pnts_t = []\n", | |
| "\n", | |
| "n = 8\n", | |
| "t0 = t1 = 0\n", | |
| "\n", | |
| "while t1-t0<1: # in seconds; if it takes too long then stop testing\n", | |
| " G = random_symmetric_graph(n)\n", | |
| " t0 = time()\n", | |
| " exhaustive_search(G)\n", | |
| " t1 = time()\n", | |
| " # record time\n", | |
| " print( f\"{n}\\t{t1-t0}\" )\n", | |
| " pnts_n.append( n )\n", | |
| " pnts_t.append( t1-t0 )\n", | |
| " n += 1" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let us plot this data to see it visually." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.plot(pnts_n, pnts_t, 'ro-')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let us see if the emperical times match the theoretical $O(n!)$ expectation.\n", | |
| "\n", | |
| "`pnts_t[i]` contains the time for `pnts_n[i]`, so we expect $\\text{pnts_t[i]} \\approx \\text{pnts_n[i]}!$.\n", | |
| "Hence, we must have\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac{\\text{pnts_t[i]}}{\\text{pnts_t[i-1]}} \\approx \\frac{i!}{(i-1)!} = i.\n", | |
| "$$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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sNm3CrVUkkeLtQjnAe7/aOdcaOAfQtdjp5IcfbN77gAOyapGqTZusBzsS2NOmwfbttu/II+Gyy2x0fcIJdpm6SLaKtw98fDAHvhMY7L3/IQE1SSKUlMDAgXaX2smToUmTsCuqth9+KN2DPWNGtAe7Wzf4xS9sdN2rl92MVyRXxDuFckKiCpEEe+ABeOsteOwxa1zOIKtW2f85kcD+4gs7D7vPPvZXGTIk2oOti0gll+lKzGw0aRL85je21sngwWFXs0fLlkUvmMnPt2VWAerWhZ49YdgwC+wePaBOnXBrFUknCvBs8913FtwdO8LTT6ddi4X3dqOC2Ja+b7+1fQ0a2Lx1ZB2Rbt3Ugy2yOwrwbBJZpGrzZlsqNg2u8S4pgblzSwf2ypW2r2lTC+rIvRyPPDJrzrOKpIQCPJsMGWI9dGPHwmGHhVJCURHMmlW6B3vdOtvXqhX07Rtt6evYMe1+QRDJKArwbDFunK1v8otf2BRKimzfvmsP9qZNtq9DBzjrrGhgt22rwBZJJAV4Nvj6a7jiCmvReOSRpL7U5s0wdWr05rvTptmFNGCXoQ8cGF0Hu2XLpJYikvMU4Jlu82a7WKdWLVukap99EvrHr19vo+rICLugwKZJatSwhZ6uu84umunVC/bfP6EvLSJ7oADPZN5bgs6dC++9Zwt9xKmwsPQ62J9/bi9Ts6a18d12m42we/aE+vUT8HcQkWpTgGeykSPhxRetUfpnP6vWH7F8eekOkfnzbXudOnahzD33WGAfc4x6sEXSjQI8UxUUwC9/CaecYhftVIL3sHBh6cBevNj21a9v0yCXXmqB3b17wmdjRCTBFOCZaN06W2KveXP4298qXMC6pMRG1LGB/f33tq9JEwvqyL0cjzpKPdgimUYBnmlKSuCSSyyJP/641JnD4uJde7DXrrV9LVvaycZIS99hh6mlTyTTKcAzzYgR8K9/wRNPsKNLDwqmRAP744/hxx/tYe3bwxlnRAO7XTsFtki2UYBnkC1vT2LqXZPI7zSO/HHnMPVW2LrV9h1+OAwYEA3sAw8Mt1YRST4FeBrbsAGmBCPsjyZsp2DG8ezkA2rM83Tp4rjmmug62E2bhl2tiKSaAjyNrFlTeh3sWbNsynvvvT1H1/6am2tO4MTHz6PnBQfRoEHY1YpI2BTgIYrcLCdy0cy8eba9dm3rwb7rLhthHzv+19R98mF45RU4P/6LdUQkOyjAU8R767mObelbuND27bcfHH+8NZdEerBr1Qqe+Oqr8OTD1u93/vmh1S8i6UcBniTew5dflr7TzHff2b7GjS2oBw+2z507w97l/SS+/NLubnDccfDggymtX0TSnwI8QYqL7d6NsSPsNWtsX4sWu/ZgV3DtTdTmzXaxTu3aNgrXZZEiUoYCvJp27LC7o8f2YG/caPvatYPTTosGdvv2VezB9h6uucYmxd9/3+6EICJShgK8krZutbWvI4H96aewZYvtO+ww6N8/ug523IsC/vWvMGYM3Hsv/PSncdcuItlJAV6BjRujPdj5+TB9ut1y0jmbs77qqmhgH3BAAl/4s8/gxhvh5z+HO+9M4B8sItlGAR5Yu9amQSKBPXNmpAcb8vKiN949/nho2DCJRfTrZ5PmL75YiYlyEcllORvgK1aUPuE4Z45tr1ULjj0Whg61wD7uOKhXLwUFRRapWrnSboHTuHEKXlREMllOBLj3sGRJ9D6O+fmwYIHt23dfG1VH5rCPPjqmBzuVhg+Hd96Bv/zFhvwiInuQlQHuPXz1VekR9rJltq9RI5u3vu46C+wuXSrowU6lCRPg7rvh4out+0REpBLiii7n3E3AVYAHZgOXe++3JaKwqigutimQ2ItmCgttX/PmFtS3326fjzgizaaWly2z4f/hh1v3idZ8FZFKqnaAO+cOBH4JHO693+qcexW4EBiVoNoqtHOnnWSM7cFev972tWkDp54a7cHu0CGNM3HHDjjvPPs8fnyKJttFJFvEO3mwN1DHObcTqAt8H39Ju9q2rXQP9pQp0R7sjh0tAyMtfW3aJKOCJLn1VvuLvfaa/UVERKqg2gHuvf/OOfcwsBTYCrzvvX8/YZXFuPZaGD3aRtJHHWXLg0QCu1mzZLxiCrz8Mjz2mPUn9usXdjUikoGc9756T3SuETAeuABYD7wGjPPe/63M4wYBgwBat27dfcmSJVV+rWnTYPVq6xbJiu66+fOt3aVLF5g0CWrWDLsiEUljzrkZ3vtd2tPimUL5KbDYe18YvMDrQE+gVIB770cCIwHy8vKq9b/FMcfEUWW62bQJzj3X5rtfeUXhLSLVFk+ALwWOdc7VxaZQ+gIFCakqW3kPgwZZj+OECbpxpYjEpdoNdd77acA4YCbWQliDYKQtFXjySRg71hap+p//CbsaEclw1Z4Dr468vDxfUJCjg/Rp0+ys68knwz/+kWbN6CKSziqaA1eKpMKaNdbreOCB8MILCm8RSYiwLyLPfsXFdon8qlXWwN6oUdgViUiWUIAn2333wXvvwVNP2d2KRUQSRL/LJ9O778KwYTBwIFx9ddjViEiWUYAny9KlMGAAdOpkS8Sm7YIsIpKpFODJsH27nbQsKrJFqurWDbsiEclCmgNPhltusZtojh8PhxwSdjUikqU0Ak+0l16CJ56wED/nnLCrEZEspgBPpHnz7GRlr14wYkTY1YhIllOAJ8qPP9oiVfvtp0WqRCQlNAeeCN7byPvrr2HiRGjZMuyKRCQHKMAT4fHHbdQ9YgT06RN2NSKSIzSFEq+pU+2E5emnw69/HXY1IpJDFODxKCy0fu9Wreyeb1qkSkRSSFMo1VVcbFdaFhbCp59qkSoRSTkFeHUNG2Z31Xn6aejaNexqRCQH6Xf+6njnHburzuWXw5VXhl2NiOQoBXhVLVli63t37mxXXGqRKhEJiQK8KrZvh379bJGqceOgTp2wKxKRHKY58Kq46SYoKIA33oAOHcKuRkRynEbglTVmjK3rfdttcNZZYVcjIqIAr5S5c2HQIOjdG37/+7CrEREBFOB7FrtI1csvw96adRKR9KA02h3vrU1wwQJbpKpFi7ArEhH5LwX47vz5z/Daa/DAA3DiiWFXIyJSiqZQKjJlCtx6K5x5pp24FBFJMwrw8qxeDeefD23awKhRulhHRNKSplDKKi6Giy6CtWttkaqGDcOuSESkXNUegTvnOjrnZsV8bHTO3ZjI4kJx9912wvLJJ6FLl7CrERGpULVH4N77r4AuAM65vYDvgDcSVFc43n4bhg+3zpPLLw+7GhGR3UrUHHhfYKH3fkmC/rzU+/ZbuOQSG3U/9ljY1YiI7FGiAvxCYGyC/qzU27bNFqkqKYHx47VIlYhkhLgD3Dm3D3AG8FoF+wc55wqccwWFhYXxvlxy3HgjzJgBL7wABx8cdjUiIpWSiBH4qcBM7/2q8nZ670d67/O893lNmzZNwMsl2IsvwlNPwe23wxlnhF2NiEilJSLA+5Op0yezZ8M110CfPnDffWFXIyJSJXEFuHOuLvAz4PXElJNCGzfaIlUNG8LYsVqkSkQyTlyp5b3fAuyfoFpSx3u44gpYtAgmTYLmzcOuSESkynJz2Pnoo9Zt8tBDcMIJYVcjIlItubcWyscf2+JUZ58Nt9wSdjUiItWWWwG+apUtUtWuHTz/vBapEpGMljtTKEVF0L8/rF8P774LDRqEXZGISFxyJ8B/+1s7YTlqFBx1VNjViIjELTemUP75TxgxAq6+Gi69NOxqREQSIvsDfNEiGDgQunWzW6SJiGSJ7A7wbdvgvPPs63HjoHbtcOsREUmg7J4D/+UvYeZMm0Jp1y7sakREEip7R+CjR8PTT8Mdd8D//V/Y1YiIJFx2BvgXX8C118JJJ8Hvfhd2NSIiSZF9Ab5hgy1S1aiRFqkSkayWXenmvd3LcvFi+PBDaNYs7IpERJImuwL8kUfgjTfsc69eYVcjIpJU2TOFkp8PQ4bY9MlNN4VdjYhI0mVHgK9cCRdcAO3bw3PPaZEqEckJmT+FUlQEF15oJy/ffx/q1w+7IhGRlMj8AP/Nb+Cjj+yO8kceGXY1IiIpk9lTKG++CQ88YDcmvuSSsKsREUmpzA3whQttkaru3e0WaSIiOSYzA3zrVujXD2rU0CJVIpKzMnMO/IYbYNYseOstaNs27GpEREKReSPw55+HZ5+FoUPhtNPCrkZEJDSZFeCzZsH110PfvjBsWNjViIiEKv0DfMwYmyapUQOOPtrmu196CfbaK+zKRERCld4BPmYMDBoES5bYQlVFRXYCc8KEsCsTEQldegf40KGwZUvpbdu323YRkRyX3gG+dGnVtouI5JC4Atw519A5N84596Vzbr5z7rhEFQZA69ZV2y4ikkPiHYH/CXjXe/8ToDMwP/6SYgwfDnXrlt5Wt65tFxHJcdUOcOdcfaA38CyA936H9359ogoDYMAAGDkS2rSxJWLbtLHvBwxI6MuIiGQi572v3hOd6wKMBOZho+8ZwK+895vLPG4QMAigdevW3ZcsWRJXwSIiucY5N8N7n1d2ezxTKHsD3YC/eO+7ApuBIWUf5L0f6b3P897nNW3aNI6XExGRWPEE+HJgufd+WvD9OCzQRUQkBaod4N77lcAy51zHYFNfbDpFRERSIN7VCG8Axjjn9gEWAZfHX5KIiFRGXAHuvZ8F7DKxLiIiyVftLpRqvZhzhUB121CaAGsSWE6iqK6qUV1Vo7qqJl3rgvhqa+O936ULJKUBHg/nXEF5bTRhU11Vo7qqRnVVTbrWBcmpLb3XQhERkQopwEVEMlQmBfjIsAuogOqqGtVVNaqratK1LkhCbRkzBy4iIqVl0ghcRERiKMBFRDJUWgS4c+4m59xc59wc59xY51ztMvtrOedecc4tcM5Nc861jdl3R7D9K+fcySmu62bn3Dzn3BfOuYnOuTYx+4qdc7OCjzdTXNdlzrnCmNe/Kmbfpc65b4KPS1Nc1x9javraObc+Zl8yj9evgprmOuduLGe/c879OXgffeGc6xazL5nHa091DQjq+cI5N8U51zlm37fOudnB8SpIcV19nHMbYn5ev43Zd0rwb3GBc26Xxe2SXNdtMTXNCd5TjYN9CTtezrnnnHOrnXNzYrY1ds5NCN4nE5xzjSp4brnvJ+dc96C+BcF70VWqGO99qB/AgcBioE7w/avAZWUecz3w1+DrC4FXgq8PBz4HagHtgIXAXims6ySgbvD1dZG6gu83hXi8LgMeL+e5jbElDxoDjYKvG6WqrjKPvwF4LgXHqxMwB6iLXXn8AXBImcf8HHgHcMCxwLQUHK/K1NUz8nrAqZG6gu+/BZqEdLz6AG+V89y9gn+DBwP7BP82D09VXWUefzrw72QcL+w+CN2AOTHbHgSGBF8PAR4o53kVvp+A6cBxwXvwHeDUytSSFiNw7AdSxzm3N/YD+r7M/jOB0cHX44C+wf9QZwIve++3e+8XAwuAHqmqy3s/yXsfuevyVKBVAl+72nXtxsnABO/9Ou/9D8AE4JSQ6uoPjE3ga1fkMGCq936L974I+Ag4u8xjzgRe8GYq0NA514LkHq891uW9nxK8LqTu/VWZ41WRHsAC7/0i7/0O4GXs2IZRV9LeX977fGBdmc2xGTUaOKucp5b7fgrea/W99596S/MXKnj+LkIPcO/9d8DDwFJgBbDBe/9+mYcdCCwLHl8EbAD2j90eWB5sS1Vdsa7E/ueMqO2cK3DOTXXOVeqHkeC6zg1+9R7nnDso2JYWxyuYamoH/Dtmc1KOFzZq6+2c2985VxcbbR9U5jEVHZekHa9K1hWr7PvLA+8752Y4u2lKolS2ruOcc587595xzh0RbEuL4xXsPwUYH7M5Wccropn3fgVA8PmAch6zu/fZ8nK271HoAR7MFZ2J/YNuCdRzzl1c9mHlPNXvZnuq6oo89mJsUa+HYja39nbZ7EXAo8659ims659AW+/9UdivmpGRQVocL2wabJz3vjhmW1KOl/d+PvAANtp5F/u1vqhs+eU9dTfbU1WXFefcSViA3x6z+XjvfTdsamWwc653Cuuaia3N0Rl4DPh7pNTy/sgU1hVxOvCJ9z52lJyU41VFCX+fhR7gwE+Bxd77Qu/9TuB1bO4v1i4hD0wAAAJrSURBVHKC/22DX88bYL/C/Hd7oBWVn05IRF04534KDAXO8N5vj2z33n8ffF4EfAh0TVVd3vu1MbU8DXQPvg79eAUupMyvt0k8Xnjvn/Xed/Pe98beN9+UeUhFxyWZx6sydeGcOwp4BjjTe7825rmR47UaeIMETh3uqS7v/Ubv/abg638BNZ1zTUiD4xXY3fsr4ccrsCqYCiH4vLqcx+zufdaqnO17Fu+EfrwfwDHAXGzO1GGjxRvKPGYwpU9ivhp8fQSlT2IuInEnMStTV1fspE3ZkzyNgFrB102wN1qiTuZUpq4WMV+fjc0dgp08WRzU1yj4unGq6goe1xE7oeRScbyCP/OA4HNr4EvKnIgETqP0SczpyT5elayrNXZep2eZ7fWA/WK+ngKcksK6mkd+flgQLg2O3d7Bv8F2RE9iHpGquoJ9kcFdvWQeL6AtpU9iPkTpk5gPlvOcCt9PwGfBey9yEvPnlaojUQc3zoMxLPiBzAFexAL5d9ioFqA28FrwZp4OHBzz3KFYiH5FJc/cJrCuD4BVwKzg481ge09gdvAGng1cmeK6RmBh+jkwCfhJzHOvCI7jAuDyVNYVPOYe4P4yz0v28ZqM3S3qc6BvsO1a4Nrgawc8EbyPZgN5KTpee6rrGeCHmPdXQbD94OA5nwc/56EprusXMe+vqcT8B4PNTX8dHMuU1hV8fxnW2BD7vIQeL2x0vwLYiY2er8TOyU3EBh8TiQZzHvDMnt5PwePmBMftcWIGOLv70KX0IiIZKh3mwEVEpBoU4CIiGUoBLiKSoRTgIiIZSgEuIpKhFOAiIhlKAS4ikqH+H5gA5XzV+nnhAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "pnts_ratios = [pnts_t[i]/pnts_t[i-1] for i in range(1,len(pnts_n))]\n", | |
| "plt.plot(pnts_n[:-1], pnts_ratios, 'ro-')\n", | |
| "plt.plot(pnts_n,pnts_n,'b-') # theoretical ratios\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "8\t0.011517763137817383\n", | |
| "9\t0.08803129196166992\n", | |
| "10\t0.8997573852539062\n", | |
| "11\t9.502121210098267\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "pnts_n = []\n", | |
| "pnts_t = []\n", | |
| "\n", | |
| "n = 8\n", | |
| "t0 = t1 = 0\n", | |
| "\n", | |
| "while t1-t0<1: # in seconds; if it takes too long then stop testing\n", | |
| " G = random_euclidean_graph(n)\n", | |
| " t0 = time()\n", | |
| " exhaustive_search(G)\n", | |
| " t1 = time()\n", | |
| " # record time\n", | |
| " print( f\"{n}\\t{t1-t0}\" )\n", | |
| " pnts_n.append( n )\n", | |
| " pnts_t.append( t1-t0 )\n", | |
| " n += 1" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24f86844d68>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.plot(pnts_n, pnts_t, 'ro-')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Discussion" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* Exhaustive search exhibits combinatorial running time $O(n!)$:\n", | |
| "* So it is only useful/possible when $n$ is small, up to about 13 on the current machine if it needs to finish within an hour." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Approximation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Greedy search" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "##### Nearest neigbours" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def greedy_nearest_neighbours(G):\n", | |
| " H = copy.deepcopy(G) # We need the original G. We work on/modify H\n", | |
| " n = len(H)\n", | |
| " cities = list(range(n))\n", | |
| " cycle = [] # solution to be built\n", | |
| " city = 0 # Start city\n", | |
| " while len(cities)>0:\n", | |
| " # Find nearest neighbour\n", | |
| " city_neighbours = H[city]\n", | |
| " smallest_distance = min(city_neighbours)\n", | |
| " nearest_city = city_neighbours.index(smallest_distance)\n", | |
| " # Update 'cycle' and 'cities' and H then 'city'\n", | |
| " cycle.append(city)\n", | |
| " cities.remove(city)\n", | |
| " for i in range(n): # 'city' is not to be used again!\n", | |
| " H[city][i] = oo\n", | |
| " H[i][city] = oo\n", | |
| " city = nearest_city\n", | |
| " return (cycle, cost(G, cycle))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The while-loop costs $O(n)$, and the nested for-loop within it also costs $O(n)$, while the rest can be assumed to cost $O(1)$.\n", | |
| "So the total cost of this greedy approach is $O(n)\\times O(n) = O(n^2)$." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Example" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " 0 1 2 3\n", | |
| " -----------------\n", | |
| " 0 | ∞ 44 50 62\n", | |
| " 1 | 44 ∞ 70 51\n", | |
| " 2 | 50 70 ∞ 13\n", | |
| " 3 | 62 51 13 ∞\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "([0, 1, 3, 2], 158)" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "G=random_symmetric_graph(4)\n", | |
| "show(G)\n", | |
| "greedy_nearest_neighbours(G)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " 0 1 2 3\n", | |
| " -----------------\n", | |
| " 0 | ∞114.3372205364464877.0778826901725482.38931969618392\n", | |
| " 1 | 114.33722053644648 ∞38.0788655293195496.42095207992918\n", | |
| " 2 | 77.0778826901725438.07886552931954 ∞68.94200461257273\n", | |
| " 3 | 82.3893196961839296.4209520799291868.94200461257273 ∞\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "([0, 2, 1, 3], 293.9670199956052)" | |
| ] | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "G=random_euclidean_graph(4)\n", | |
| "show(G)\n", | |
| "greedy_nearest_neighbours(G)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Test time and quality cost" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We will initially estimate the \"quality\" simply by comparing the length of the cycle found by the greed approach to the \"average length of a random cycle\".\n", | |
| "\n", | |
| "The average length of a random cycle is estimated to be the average length of a graph edge multiplied by the number of edges, i.e.\n", | |
| "\n", | |
| "$$\\frac{\\text{MAX_DISTANCE}}{2} \\times n.$$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "10\t0.0496525764465332\t0.5082679999999999\n", | |
| "20\t0.1907353401184082\t0.319404\n", | |
| "30\t0.3874533176422119\t0.24130666666666664\n", | |
| "40\t0.7231287956237793\t0.19880899999999999\n", | |
| "50\t1.103902816772461\t0.1685712\n", | |
| "60\t1.5338325500488281\t0.14980266666666667\n", | |
| "70\t1.8535380363464355\t0.13568571428571427\n", | |
| "80\t2.7287096977233887\t0.123572\n", | |
| "90\t3.5187318325042725\t0.11156755555555554\n", | |
| "100\t4.073294162750244\t0.10502280000000001\n", | |
| "110\t4.978093862533569\t0.09801854545454546\n", | |
| "120\t6.004432439804077\t0.092656\n", | |
| "130\t7.973381757736206\t0.087268\n", | |
| "140\t8.291640758514404\t0.08288485714285714\n", | |
| "150\t8.916183471679688\t0.0789024\n", | |
| "160\t10.312152862548828\t0.07590525\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "MAX_REPETITIONS = 500\n", | |
| "\n", | |
| "pnts_n = []\n", | |
| "pnts_t = []\n", | |
| "pnts_q = []\n", | |
| "\n", | |
| "n = 10\n", | |
| "t = 0\n", | |
| "\n", | |
| "while t<10: # in seconds; if it takes too long then stop testing\n", | |
| " t = 0\n", | |
| " expected_cycle_length = (MAX_DISTANCE/2)*n # TODO: Better estimate?\n", | |
| " sum_of_distances = 0\n", | |
| " for repetitions in range(MAX_REPETITIONS):\n", | |
| " G = random_symmetric_graph(n)\n", | |
| " t0 = time()\n", | |
| " cycle, length = greedy_nearest_neighbours(G)\n", | |
| " t1 = time()\n", | |
| " sum_of_distances += length\n", | |
| " t += t1-t0\n", | |
| " # record time and quality\n", | |
| " q = (sum_of_distances/MAX_REPETITIONS)/expected_cycle_length\n", | |
| " pnts_n.append( n )\n", | |
| " pnts_t.append( t )\n", | |
| " pnts_q.append( q )\n", | |
| " print( f\"{n}\\t{t}\\t{q}\" )\n", | |
| " n += 10" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 24, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
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ZV/erX10+idm3gWtrGuVSXFzspaWlaV9PJPbWrIExY+Cuu2DnTrjkkhDmbdtGXZnkITOb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\nRCQhFOgiIgnx/xmt4HL/3qYvAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24f870a6940>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.plot(pnts_n, pnts_t, 'ro-')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 25, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24f8710d6a0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.plot(pnts_n, pnts_q, 'ro-')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "10\t0.06430292129516602\t143.42119902704604\n", | |
| "20\t0.17042922973632812\t142.83896438342083\n", | |
| "30\t0.48963046073913574\t142.04123881625168\n", | |
| "40\t0.776923418045044\t141.14993333691754\n", | |
| "50\t1.0704421997070312\t140.34975785979168\n", | |
| "60\t1.514561414718628\t140.55813580788916\n", | |
| "70\t2.0883636474609375\t138.78251853848585\n", | |
| "80\t2.5483763217926025\t138.66092482047029\n", | |
| "90\t3.320828914642334\t137.51922662536322\n", | |
| "100\t4.0691139698028564\t138.2574686102402\n", | |
| "110\t4.8069376945495605\t137.42881961473122\n", | |
| "120\t5.977138519287109\t137.11342466295704\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "MAX_REPETITIONS = 500\n", | |
| "\n", | |
| "pnts_n = []\n", | |
| "pnts_t = []\n", | |
| "pnts_q = []\n", | |
| "\n", | |
| "n = 10\n", | |
| "t = 0\n", | |
| "\n", | |
| "while t<5: # in seconds; if it takes too long then stop testing\n", | |
| " t = 0\n", | |
| " expected_cycle_length = 0.71*sqrt(n) # TODO: Better estimate?\n", | |
| " sum_of_distances = 0\n", | |
| " for repetitions in range(MAX_REPETITIONS):\n", | |
| " G = random_euclidean_graph(n)\n", | |
| " t0 = time()\n", | |
| " cycle, length = greedy_nearest_neighbours(G)\n", | |
| " t1 = time()\n", | |
| " sum_of_distances += length\n", | |
| " t += t1-t0\n", | |
| " # record time and quality\n", | |
| " q = (sum_of_distances/MAX_REPETITIONS)/expected_cycle_length\n", | |
| " pnts_n.append( n )\n", | |
| " pnts_t.append( t )\n", | |
| " pnts_q.append( q )\n", | |
| " print( f\"{n}\\t{t}\\t{q}\" )\n", | |
| " n += 10" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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BW2/d+uBx+fLQu9aDR8kxjXGLZFJ/q9X33w9DIq1awZVXhlPW9eBRYqDgFskk3VarAPvs\nE2aMiMSkwaESM9vPzGaZ2dtmtsjMLsxFYSKxy7Sl6ocf5rYOkXqy6XFvAi529/8ysz2A+WY2w93f\njrg2kXi4wwMPZH5fW61KzBrscbv7R+7+X6mv1wGLgX2jLkwkFtXVcOKJMGoU7L8/7Lbbtu9rq1XJ\nA42aVWJmJUB/YG6a98aYWaWZVVZXVzdPdSK5NHVq2Efk2Wfhpptg0aKwGZS2WpU8Y57p5I36N5q1\nA14Exrv7lB3dW1pa6pWVlc1QnkgOrF8flqaXlYV52A8+qPnYknNmNt/dS7O5N6set5m1Ap4AKhoK\nbZFEeeUV6Ncv9KzHjoV58xTakveymVViQDmw2N1vib4kkRzYsCFM9/vRj2DzZpg9GyZMgDZt4q5M\npEHZ9LgHAWcCPzGzhamPYRHXJRKdt9+GgQPhuuvCQ8g33oAf/zjuqkSy1uB0QHefA1gOahGJ1pYt\n8B//AZdeCu3bw1NPwYgRcVcl0mhaOSnFoaoKzj4bZs6E444LY9qdO8ddlchO0SZTUtjc4aGHwgPH\nefNCYD/9tEJbEk3BLYXrs8/g5z+HM8+Evn3DWPa554Y52SIJpuCWwlR7KO9TT8H118OLL8K3vhV3\nVSLNQsEtheWLL+Bf/gWOPRb23jsMj1x6KbRsGXdlIs1GwS2FY+5c6N8f7r47rISsrAyLa0QKjIJb\nkm/jxnCwwaBB4eDemTPh5pth113jrkwkEgpuSZb6R4nddFM4Ruzqq8PmT2++CUOGxFykSLQ0j1uS\nI91RYmPHhsN6H388bMcqUgTU45bkyHSUWIcOCm0pKgpuSQ4dJSYCaKhEkqCmBm67LfP7OkpMiox6\n3JK/Nm0KS9R79QpzsQ86aPuZIjpKTIqQglvyjztMmRKWqY8ZE44Me+klWLAAJk3SUWJS9DRUIvll\n9uzQu547F/r0CUvWjz9+6/4iI0cqqKXoqcct+eGNN2DYMBg6FFauhPLyMCd7xAhtCiVSj4Jb4rV8\nedi9r39/eO01uPFGeOcdOOcc2EX/IRRJR38zJB7V1XDttXDXXWEDqEsuCYtp9tor7spE8p6CW3Jr\n/Xq45Zawl0hNTehZX3kl7Ltv3JWJJIaCW3Jjw4Ywte/qq2HVKjjhhDCNr3fvuCsTSRwFt0RryxZ4\n7LGwXH3ZMhg8GJ55Bg45JO7KRBJLDyclOjNmQGkpnHYatGsH06bBrFkKbZEmUnBL09TfZrWiIhxg\ncMQRcNRR8D//Aw8+GBbPHHuspvaJNAMNlcjOS7fN6llnwebN0LEjTJwI550HbdrEW6dIgWkwuM1s\nMjAcWOXufaMvSRIj3TarmzfDnnvC0qXQvn08dYkUuGyGSu4Djom4DkmaqqrQw05n7VqFtkiEGgxu\nd38JWJ2DWiTfrV4dNnUaPDhs8JSJtlkViZTGuGXHvvwS/vSnMJ795z+Hg3l794ZrrglHhl1xxbbD\nJdpmVSRyzRbcZjYGGAPQXT2uZNu0KZyU/vDDYXvVdeuga1e44AI4/fSwr0jt7JB99glj3VVVoac9\nfrx27xOJmLl7wzeZlQBTs304WVpa6pWVlU2rTHLLPUzjq6iARx+FTz4JDxlPPDEE8eDBYU8REYmE\nmc1399Js7tVQSbH7+99DWD/8cPi6dWsYPjyE9bBh2584IyKxy2Y64CPAEKCjmX0AXOnu5VEXJhH6\n+OPQq374YXj99TDsMXRoOMDghBPCqekikreymVVymrt3cfdW7t5NoZ0A6VYzrl0L998fVjPuuy9c\ndFEYy775ZlixAl54IezUp9AWyXsaKik0mVYzmoWg7tkTLr88PGTs0yfeWkVkpyi4C83ll6dfzdiu\nHTz/PAwcqP1CRBJOwV0I3MNY9SOPhGl56XzxBRx6aG7rEpFIKLiTbNGiENaPPhr2BmndGnbbLSya\nqU9z60UKhrZ1TZply+C66+DAA6FvX7j+evjWt2Dy5DD3+p57wurFurSaUaSgqMedBB99FE6ReeQR\nmDs3XDvsMLjtNjj5ZOjceeu9tasWtZpRpGApuPPV6tXwxBMhrGfPDuPY/frBhAnw85/veJOnkSMV\n1CIFTMGdT9avD+cxPvIIPPdc2NCpVy/4zW/g1FM1fU9EAAV3/L7+Ouy69+ijIbS//BK6dQsbOp12\nGhx8sKbvicg2FNy5UFGx7ZjzNdfAN78ZetZTpsCaNeGor1GjQlgPGhRWPYqIpKHgjlq6lYy/+EX4\neo894Gc/C2F9+OHQqlV8dYpIYii4o3bJJduvZITQw66qCvOuRUQaQf8fj8KGDWFGyLBhsHJl+ns+\n+0yhLSI7RT3u5rRoEZSXw4MPwqefhl342rcPO/PVp5WMIrKT1ONuqrVrw2rFgQPDSsbbbw+nxUyb\nFsaz77xTKxlFpFmpx70z3GHOnNC7/uMfwxj2AQfA734HZ54JnTptvVcrGUWkmSm4G+Ojj8JhBJMn\nh2O+9tgjBPDo0TBgQOb51lrJKCLNSMHdkI0bw7BHeXn4vHkz/OhHoQd90kmw++5xVygiRUbBncmS\nJaFn/cADYde9Ll3g3/4Nzj4b9t8/7upEpIgV58PJdGcyQtgrZPJk+OEPw74gt94aDh945pkwPn39\n9QptEYld8fW4061kHD06zAyZPz+E93e/CzfeGFY41t0yVUQkDxRfcKc7k/Hrr+Gll8IwyOjRoZet\njZ1EJE8VXnBv3gwffgjLl2/9eO+9rV+vWJH515aX56xMEZGdlT/BXX8HvUxznd2hunrbYK4b0O+/\nH2aC1DKDrl2hZ08YMiSMV69Zs/331UpGEUmIrILbzI4BJgItgUnufkOzVpFu3Pncc6GyEvbbb/tw\nrj/U0alTCOYf/ABOPDF8XfvRvTu0aZP5Z4FWMopIopi77/gGs5bAO8CRwAfA68Bp7v52pl9TWlrq\nlZWV2VdRUhLCOpP27bcN49qPkpLw0a5d9j8Lsu/di4jkiJnNd/fSbO7Npsc9AHjX3ZelvvmjwAgg\nY3A3WlVV+utmYRe9vfZqth8FaCWjiCRaNvO49wXqPtH7IHWt+WQaX+7evflDW0Qk4ZptAY6ZjTGz\nSjOrrK6ubtwvHj9eO+iJiGQpm+BeCexX53W31LVtuHuZu5e6e2mnurvjZWPkSCgrgx49wvBIjx7h\ntYYzRES2k80Y9+tALzPrSQjsU4HTm70SjTuLiGSlweB2901m9v+A5wjTASe7+6LIKxMRkbSymsft\n7tOAaRHXIiIiWSjO3QFFRBJMwS0ikjAKbhGRhGlwyftOfVOzamAHa9jzRkfg07iLiFAht09tS65C\nbl9T2tbD3bOaSx1JcCeFmVVmuzdAEhVy+9S25Crk9uWqbRoqERFJGAW3iEjCFHtwl8VdQMQKuX1q\nW3IVcvty0raiHuMWEUmiYu9xi4gkTtEEt5ntZ2azzOxtM1tkZhemru9tZjPM7O+pz4ndANzMWprZ\nAjObmnpdSG3rYGaPm9kSM1tsZocWSvvM7KLUn8m3zOwRM9s1qW0zs8lmtsrM3qpzLWNbzOwyM3vX\nzP5mZkfHU3X2MrTvptSfyzfN7Ekz61DnvUjaVzTBDWwCLnb3A4CBwPlmdgBwKfCCu/cCXki9TqoL\ngcV1XhdS2yYC0929N3AQoZ2Jb5+Z7QtcAJS6e1/CRm6nkty23QccU+9a2rak/v6dCnwv9WvuTB2V\nmM/uY/v2zQD6uvuBhGMeL4OI2+fuRfkBPE04R/NvQJfUtS7A3+KubSfb043wl+InwNTUtUJp257A\nclLPZOpcT3z72HrC1N6ETd+mAkcluW1ACfBWQ79PqYC7rM59zwGHxl1/Y9tX772fARVRt6+Yetz/\nx8xKgP7AXKCzu3+UeutjoHNMZTXV74GxwJY61wqlbT2BauDe1FDQJDPbnQJon7uvBG4GqoCPgDXu\n/jwF0LY6MrUl+mMRc+8c4M+pryNrX9EFt5m1A54A/r+7r637nod/FhM3zcbMhgOr3H1+pnuS2raU\nXYCDgbvcvT/wBfWGDpLavtR47wjCP05dgd3N7Iy69yS1bekUUlvqM7NxhCHZiqh/VlEFt5m1IoR2\nhbtPSV3+xMy6pN7vAqyKq74mGAQcb2bvAY8CPzGzhyiMtkHoqXzg7nNTrx8nBHkhtO8IYLm7V7v7\nRmAKcBiF0bZamdqS1bGISWBmo4DhwMjUP04QYfuKJrjNzIByYLG731LnrWeAs1Jfn0UY+04Ud7/M\n3bu5ewnhYchMdz+DAmgbgLt/DKwws++mLh0OvE1htK8KGGhmbVN/Rg8nPHgthLbVytSWZ4BTzaxN\n6mjEXsC8GOprEjM7hjBMeby719R5K7r2xT3Qn8MHCj8k/BftTWBh6mMY8A3CQ72/A38B9o671ia2\ncwhbH04WTNuAfkBl6vfvKWCvQmkf8FtgCfAW8CDQJqltAx4hjNVvJPxPafSO2gKMA5YSHmAeG3f9\nO9m+dwlj2bW5cnfU7dPKSRGRhCmaoRIRkUKh4BYRSRgFt4hIwii4RUQSRsEtIpIwCm4RkYRRcIuI\nJIyCW0QkYf4X3GmNLIbrjy8AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24f8382db38>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.plot(pnts_n, pnts_t, 'ro-')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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Ouou4iUT4RdAId+JqjF5EJJtWrw67dSUSYZjHLKyLv2RJWP2zQmFhne9L0Bi9\niEguaN8exoyBf/4T3nsvjO2///7WIQ9ZvRO3xqA3sylmttrMFqUcu8HM3jSz+Wb2nJntmjxekjxW\n8bXZzA7MSuUiIvlmzz1D0G/aVPXrWVogrjY9+qnAoErHJrp7D3c/EJgFXAvg7gl3PzB5/EzgI3ef\nn8mCRUTyXrqF4LK0QFyNQe/uLwJrKh37IuVpa6Cqgf4RwPQGVSciEkeNvEBcvcfozWyCmS0HSkj2\n6Cs5HZhWzc+PNrNSMystLy+vbxkiIvmnkReIq9WsGzMrAma5+zar+ZjZWGB7dx+fcqw38IC7d69N\nEZp1IyJSd4056yYBnFzp2BlU05sXEZHGU6+gN7M9U54OA95Jea0AOA2Nz4uI5IQatxI0s2lAP6Ct\nma0AxgODzWxvYDNQBlyQ8iNHAMvd/cPMlysiInVVY9C7+4gqDqfd9t3d/w70aUBNIiKSQbozVkQk\n5nJirRszKycMAeWDtsCnUReRRXFun9qWv+Lcvoa0rau7t6vppJwI+nxiZqW1mc6Ur+LcPrUtf8W5\nfY3RNg3diIjEnIJeRCTmFPR1NynqArIszu1T2/JXnNuX9bZpjF5EJObUoxcRiTkFfTXMrLOZ/c3M\n3jKzxWb2k+Tx/zKz583s/eT3NlHXWl9m1szM3jCzWcnnsWibmX3XzGaY2Ttm9raZHRKjtl2S/Pe4\nyMymmdn2+dy2NJsbpW2PmY01sw/M7F0zGxhN1bWTpm0Tk/8u3zSzmWb23ZTXstI2BX31NgKXuvu+\nhLt9LzKzfYErgRfcfU/gheTzfPUT4O2U53Fp22+AZ919H+AAQhvzvm1mthvwY6A4uZpsM8Iigvnc\ntqlsu7lRle1J/v93BrBf8mfuNrNmjVdqnU1l27Y9D+zv7j2A94CxkN22Keir4e6fuPu85OMvCWGx\nG2Eht4eSpz0EnBhNhQ1jZp2A44EHUg7nfdvMbCfCmkuTAdz9G3f/nBi0Lak50MrMmgOFwMfkcduq\n2tyI9O0ZBkx39w3u/hHwAdCrUQqthzQbNz3n7huTT/8JdEo+zlrbFPS1lFyT/yDgNaCDu3+SfGkV\n0CGishrqduAKwuJ0FeLQtm5AOfBgcljqATNrTQza5u4rgVuAZcAnwH/c/Tli0LZK0rVnN2B5ynkr\nksfy1TnAn5OPs9Y2BX0tmNkOwO+Bn1baRhEP05bybuqSmQ0BVrv73HTn5GvbCD3ensA97n4QsJZK\nQxn52raH6DxOAAABjUlEQVTkWPUwwi+zXYHWZjYy9Zx8bVs6cWtPBTMbRxgeTmT7sxT0NTCzFoSQ\nT7j7H5KH/2VmuyRf3wVYHVV9DXAYMNTMlhL2DjjazB4lHm1bAaxw99eSz2cQgj8ObTsG+Mjdy939\nW+APwKHEo22p0rVnJdA55bxOyWN5xcx+BAwBSnzLHPestU1BXw0zM8I479vufmvKS08DZyUfnwU8\n1di1NZS7j3X3Tu5eRLgANNvdRxKPtq0Clif3TADoD7xFDNpGGLLpY2aFyX+f/QnXjuLQtlTp2vM0\ncIaZbWdm3YA9gdcjqK/ezGwQYch0qLuvS3kpe21zd32l+QL6Ev5kfBOYn/waDOxMmAnwPvBX4L+i\nrrWB7exH2BOYuLQNOBAoTf63exJoE6O2/Zywq9si4BFgu3xuG2Hb0U+Abwl/jY2qrj3AOGAJ8C5w\nXNT116NtHxDG4isy5d5st013xoqIxJyGbkREYk5BLyIScwp6EZGYU9CLiMScgl5EJOYU9CIiMaeg\nFxGJOQW9iEjM/X/K1H+jH/37WQAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x24f870031d0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.plot(pnts_n, pnts_q, 'ro-')\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Metaheuristics" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "............................................................\n", | |
| "\n", | |
| "............................................................\n", | |
| "\n", | |
| "............................................................\n", | |
| "\n", | |
| "............................................................\n", | |
| "\n", | |
| "............................................................\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# References" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "* Garey, S. and Johnson, D. (1979)\n", | |
| "**Computers and Intractability: A Guide to the Theory of NP-Completeness.**\n", | |
| "Freeman.\n", | |
| "\n", | |
| "* Hoos, H. and Stutzler, T. (2005)\n", | |
| "**Stochastic Local Search: Foundations and Applications.**\n", | |
| "Morgan Kaufmann.\n", | |
| "\n", | |
| "* Sipser, M. (2013).\n", | |
| "**Introduction to the theory of computation**\n", | |
| "(3rd international ed.). Cengage Learning." | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.6.1" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |