433 lines
122 KiB
Plaintext
433 lines
122 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>File Name</th>\n",
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" <th>Line 1</th>\n",
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" <th>Line 2</th>\n",
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" <th>Line 3</th>\n",
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" <th>Line 4</th>\n",
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" <th>Line 5</th>\n",
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" <th>Line 6</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>CHN144.tsp</td>\n",
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" <td>NAME : CHN144</td>\n",
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" <td>COMMENT : China 144-city problem</td>\n",
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" <td>TYPE : TSP</td>\n",
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" <td>DIMENSION : 144</td>\n",
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" <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>eil101.tsp</td>\n",
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" <td>NAME : eil101</td>\n",
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" <td>COMMENT : 101-city problem (Christofides/Eilon)</td>\n",
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" <td>TYPE : TSP</td>\n",
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" <td>DIMENSION : 101</td>\n",
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" <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>eil76.tsp</td>\n",
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" <td>NAME: eil76</td>\n",
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" <td>TYPE: TSP</td>\n",
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" <td>COMMENT: 76-city problem (Christofides/Eilon)</td>\n",
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" <td>DIMENSION: 76</td>\n",
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" <td>EDGE_WEIGHT_TYPE: EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>GR96.tsp</td>\n",
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" <td>NAME: gr96</td>\n",
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" <td>TYPE: TSP</td>\n",
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" <td>COMMENT: Africa-Subproblem of 666-city TSP (Gr...</td>\n",
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" <td>DIMENSION: 96</td>\n",
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" <td>EDGE_WEIGHT_TYPE: GEO</td>\n",
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" <td>DISPLAY_DATA_TYPE: COORD_DISPLAY</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>PBK411.tsp</td>\n",
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" <td>NAME : pbk411</td>\n",
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" <td>COMMENT : Bonn VLSI data set with 411 points</td>\n",
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" <td>TYPE : TSP</td>\n",
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" <td>DIMENSION : 411</td>\n",
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" <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>PR76.tsp</td>\n",
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" <td>NAME : pr76</td>\n",
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" <td>COMMENT : 76-city problem (Padberg/Rinaldi)</td>\n",
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" <td>TYPE : TSP</td>\n",
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" <td>DIMENSION : 76</td>\n",
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" <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>RBU737.tsp</td>\n",
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" <td>NAME : rbu737</td>\n",
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" <td>COMMENT : Bonn VLSI data set with 737 points</td>\n",
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" <td>TYPE : TSP</td>\n",
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" <td>DIMENSION : 737</td>\n",
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" <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>ulysses16.tsp</td>\n",
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" <td>NAME: ulysses16.tsp</td>\n",
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" <td>TYPE: TSP</td>\n",
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" <td>COMMENT: Odyssey of Ulysses (Groetschel/Padberg)</td>\n",
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" <td>DIMENSION: 16</td>\n",
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" <td>EDGE_WEIGHT_TYPE: GEO</td>\n",
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" <td>DISPLAY_DATA_TYPE: COORD_DISPLAY</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>ulysses8.tsp</td>\n",
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" <td>NAME: ulysses16.tsp</td>\n",
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" <td>TYPE: TSP</td>\n",
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" <td>COMMENT: Odyssey of Ulysses (Groetschel/Padberg)</td>\n",
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" <td>DIMENSION: 8</td>\n",
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" <td>EDGE_WEIGHT_TYPE: GEO</td>\n",
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" <td>DISPLAY_DATA_TYPE: COORD_DISPLAY</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>XIT1083.tsp</td>\n",
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" <td>NAME : xit1083</td>\n",
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" <td>COMMENT : Bonn VLSI data set with 1083 points</td>\n",
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" <td>TYPE : TSP</td>\n",
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" <td>DIMENSION : 1083</td>\n",
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" <td>EDGE_WEIGHT_TYPE : EUC_2D</td>\n",
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" <td>NODE_COORD_SECTION</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" File Name Line 1 \\\n",
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"0 CHN144.tsp NAME : CHN144 \n",
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"1 eil101.tsp NAME : eil101 \n",
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"2 eil76.tsp NAME: eil76 \n",
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"3 GR96.tsp NAME: gr96 \n",
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"4 PBK411.tsp NAME : pbk411 \n",
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"5 PR76.tsp NAME : pr76 \n",
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"6 RBU737.tsp NAME : rbu737 \n",
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"7 ulysses16.tsp NAME: ulysses16.tsp \n",
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"8 ulysses8.tsp NAME: ulysses16.tsp \n",
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"9 XIT1083.tsp NAME : xit1083 \n",
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"\n",
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" Line 2 \\\n",
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"0 COMMENT : China 144-city problem \n",
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"1 COMMENT : 101-city problem (Christofides/Eilon) \n",
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"2 TYPE: TSP \n",
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"3 TYPE: TSP \n",
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"4 COMMENT : Bonn VLSI data set with 411 points \n",
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"5 COMMENT : 76-city problem (Padberg/Rinaldi) \n",
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"6 COMMENT : Bonn VLSI data set with 737 points \n",
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"7 TYPE: TSP \n",
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"8 TYPE: TSP \n",
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"9 COMMENT : Bonn VLSI data set with 1083 points \n",
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"\n",
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" Line 3 Line 4 \\\n",
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"0 TYPE : TSP DIMENSION : 144 \n",
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"1 TYPE : TSP DIMENSION : 101 \n",
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"2 COMMENT: 76-city problem (Christofides/Eilon) DIMENSION: 76 \n",
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"3 COMMENT: Africa-Subproblem of 666-city TSP (Gr... DIMENSION: 96 \n",
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"4 TYPE : TSP DIMENSION : 411 \n",
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"5 TYPE : TSP DIMENSION : 76 \n",
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"6 TYPE : TSP DIMENSION : 737 \n",
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"7 COMMENT: Odyssey of Ulysses (Groetschel/Padberg) DIMENSION: 16 \n",
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"8 COMMENT: Odyssey of Ulysses (Groetschel/Padberg) DIMENSION: 8 \n",
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"9 TYPE : TSP DIMENSION : 1083 \n",
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"\n",
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" Line 5 Line 6 \n",
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"0 EDGE_WEIGHT_TYPE : EUC_2D NODE_COORD_SECTION \n",
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"1 EDGE_WEIGHT_TYPE : EUC_2D NODE_COORD_SECTION \n",
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"2 EDGE_WEIGHT_TYPE: EUC_2D NODE_COORD_SECTION \n",
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"3 EDGE_WEIGHT_TYPE: GEO DISPLAY_DATA_TYPE: COORD_DISPLAY \n",
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"4 EDGE_WEIGHT_TYPE : EUC_2D NODE_COORD_SECTION \n",
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"5 EDGE_WEIGHT_TYPE : EUC_2D NODE_COORD_SECTION \n",
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"6 EDGE_WEIGHT_TYPE : EUC_2D NODE_COORD_SECTION \n",
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"7 EDGE_WEIGHT_TYPE: GEO DISPLAY_DATA_TYPE: COORD_DISPLAY \n",
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"8 EDGE_WEIGHT_TYPE: GEO DISPLAY_DATA_TYPE: COORD_DISPLAY \n",
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"9 EDGE_WEIGHT_TYPE : EUC_2D NODE_COORD_SECTION "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"\n",
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"# 定义文件夹路径\n",
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"folder_path = './data'\n",
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"\n",
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"# 初始化一个空的DataFrame来存储信息\n",
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"columns = ['File Name', 'Line 1', 'Line 2', 'Line 3', 'Line 4', 'Line 5', 'Line 6']\n",
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"df = pd.DataFrame(columns=columns)\n",
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"\n",
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"# 遍历文件夹中的所有文件\n",
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"for file_name in os.listdir(folder_path):\n",
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" if file_name.endswith('.tsp'):\n",
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" file_path = os.path.join(folder_path, file_name)\n",
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" with open(file_path, 'r') as file:\n",
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" lines = file.readlines()\n",
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" # 取前六行作为基本信息\n",
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" basic_info = lines[:6]\n",
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" # 如果行数不足六行，用空字符串填充\n",
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" while len(basic_info) < 6:\n",
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" basic_info.append('')\n",
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" # 将信息添加到DataFrame中\n",
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" new_row = pd.DataFrame({\n",
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" 'File Name': [file_name],\n",
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" 'Line 1': [basic_info[0].strip()],\n",
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" 'Line 2': [basic_info[1].strip()],\n",
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" 'Line 3': [basic_info[2].strip()],\n",
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" 'Line 4': [basic_info[3].strip()],\n",
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" 'Line 5': [basic_info[4].strip()],\n",
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" 'Line 6': [basic_info[5].strip()]\n",
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" })\n",
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" df = pd.concat([df, new_row], ignore_index=True)\n",
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"\n",
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"# 显示表格\n",
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"df\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 800x800 with 2 Axes>"
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|
]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
|
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"source": [
|
|
"import numpy as np\n",
|
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"import matplotlib.pyplot as plt\n",
|
|
"from matplotlib.path import Path\n",
|
|
"import matplotlib.patches as patches\n",
|
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"import os\n",
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"\n",
|
|
"# 确保plot文件夹存在\n",
|
|
"if not os.path.exists('./plot'):\n",
|
|
" os.makedirs('./plot')\n",
|
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"\n",
|
|
"# 设置中文显示\n",
|
|
"plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签\n",
|
|
"plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
|
|
"\n",
|
|
"# 生成10个随机分布的节点\n",
|
|
"np.random.seed(2025)\n",
|
|
"n_points = 10\n",
|
|
"points = np.random.rand(n_points, 2) * 100\n",
|
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"\n",
|
|
"# 计算两点间距离的函数\n",
|
|
"def calculate_distance(route):\n",
|
|
" total = 0\n",
|
|
" for i in range(len(route)):\n",
|
|
" j = (i + 1) % len(route)\n",
|
|
" city1 = points[route[i]]\n",
|
|
" city2 = points[route[j]]\n",
|
|
" total += np.sqrt(np.sum((city1 - city2) ** 2))\n",
|
|
" return total\n",
|
|
"\n",
|
|
"# 生成随机解\n",
|
|
"random_solution = list(range(n_points))\n",
|
|
"np.random.shuffle(random_solution)\n",
|
|
"random_distance = calculate_distance(random_solution)\n",
|
|
"\n",
|
|
"# 使用启发式算法求解TSP问题\n",
|
|
"def heuristic_solution():\n",
|
|
" n = n_points\n",
|
|
" # 初始解:从城市0开始\n",
|
|
" current_solution = [0]\n",
|
|
" unvisited = set(range(1, n))\n",
|
|
" \n",
|
|
" # 不断选择最近的未访问城市\n",
|
|
" while unvisited:\n",
|
|
" current = current_solution[-1]\n",
|
|
" # 找到距离当前城市最近的未访问城市\n",
|
|
" next_city = min(unvisited, \n",
|
|
" key=lambda x: np.sqrt(np.sum((points[current] - points[x]) ** 2)))\n",
|
|
" current_solution.append(next_city)\n",
|
|
" unvisited.remove(next_city)\n",
|
|
" \n",
|
|
" # 2-opt局部搜索优化\n",
|
|
" improved = True\n",
|
|
" while improved:\n",
|
|
" improved = False\n",
|
|
" for i in range(n-2):\n",
|
|
" for j in range(i+2, n):\n",
|
|
" # 计算当前路径长度\n",
|
|
" old_distance = (\n",
|
|
" np.sqrt(np.sum((points[current_solution[i]] - points[current_solution[i+1]]) ** 2)) +\n",
|
|
" np.sqrt(np.sum((points[current_solution[j]] - points[current_solution[(j+1)%n]]) ** 2))\n",
|
|
" )\n",
|
|
" # 计算交换后的路径长度\n",
|
|
" new_distance = (\n",
|
|
" np.sqrt(np.sum((points[current_solution[i]] - points[current_solution[j]]) ** 2)) +\n",
|
|
" np.sqrt(np.sum((points[current_solution[i+1]] - points[current_solution[(j+1)%n]]) ** 2))\n",
|
|
" )\n",
|
|
" \n",
|
|
" if new_distance < old_distance:\n",
|
|
" # 如果交换后更优,则进行2-opt交换\n",
|
|
" current_solution[i+1:j+1] = reversed(current_solution[i+1:j+1])\n",
|
|
" improved = True\n",
|
|
" break\n",
|
|
" if improved:\n",
|
|
" break\n",
|
|
" \n",
|
|
" # 添加回到起点\n",
|
|
" current_solution.append(0)\n",
|
|
" return current_solution\n",
|
|
"\n",
|
|
"better_solution = heuristic_solution()\n",
|
|
"better_distance = calculate_distance(better_solution)\n",
|
|
"\n",
|
|
"# 创建并保存随机解图\n",
|
|
"plt.figure(figsize=(8, 8))\n",
|
|
"ax1 = plt.gca()\n",
|
|
"for i, point in enumerate(points):\n",
|
|
" ax1.scatter(point[0], point[1], c='blue', s=100)\n",
|
|
" ax1.annotate(f'城市{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 16)\n",
|
|
"\n",
|
|
"for i in range(len(random_solution)):\n",
|
|
" j = (i + 1) % len(random_solution)\n",
|
|
" city1 = points[random_solution[i]]\n",
|
|
" city2 = points[random_solution[j]]\n",
|
|
" ax1.plot([city1[0], city2[0]], [city1[1], city2[1]], 'r-')\n",
|
|
"\n",
|
|
"ax1.set_title(f'总距离: {random_distance:.2f}',fontsize = 20)\n",
|
|
"ax1.grid(True)\n",
|
|
"ax1.margins(0.13)\n",
|
|
"plt.savefig('./plot/random_solution.png',dpi=300)\n",
|
|
"plt.close()\n",
|
|
"\n",
|
|
"# 创建并保存贪心解图\n",
|
|
"plt.figure(figsize=(8, 8))\n",
|
|
"ax2 = plt.gca()\n",
|
|
"for i, point in enumerate(points):\n",
|
|
" ax2.scatter(point[0], point[1], c='blue', s=100)\n",
|
|
" ax2.annotate(f'城市{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 16)\n",
|
|
"\n",
|
|
"for i in range(len(better_solution)):\n",
|
|
" j = (i + 1) % len(better_solution)\n",
|
|
" city1 = points[better_solution[i]]\n",
|
|
" city2 = points[better_solution[j]]\n",
|
|
" ax2.plot([city1[0], city2[0]], [city1[1], city2[1]], 'g-')\n",
|
|
"\n",
|
|
"ax2.set_title(f'总距离: {better_distance:.2f}',fontsize = 20)\n",
|
|
"ax2.grid(True)\n",
|
|
"ax2.margins(0.13)\n",
|
|
"plt.savefig('./plot/better_solution.png')\n",
|
|
"plt.close()\n",
|
|
"\n",
|
|
"# 显示两个图\n",
|
|
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 8))\n",
|
|
"\n",
|
|
"# 绘制随机解\n",
|
|
"for i, point in enumerate(points):\n",
|
|
" ax1.scatter(point[0], point[1], c='blue', s=100)\n",
|
|
" ax1.annotate(f'城市{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 16)\n",
|
|
"\n",
|
|
"for i in range(len(random_solution)):\n",
|
|
" j = (i + 1) % len(random_solution)\n",
|
|
" city1 = points[random_solution[i]]\n",
|
|
" city2 = points[random_solution[j]]\n",
|
|
" ax1.plot([city1[0], city2[0]], [city1[1], city2[1]], 'r-')\n",
|
|
"\n",
|
|
"ax1.set_title(f'总距离: {random_distance:.2f}',fontsize = 20)\n",
|
|
"ax1.grid(True)\n",
|
|
"ax1.margins(0.13)\n",
|
|
"\n",
|
|
"# 绘制贪心解\n",
|
|
"for i, point in enumerate(points):\n",
|
|
" ax2.scatter(point[0], point[1], c='blue', s=100)\n",
|
|
" ax2.annotate(f'城市{i}', (point[0], point[1]), xytext=(5, 5), textcoords='offset points',fontsize = 16)\n",
|
|
"\n",
|
|
"for i in range(len(better_solution)):\n",
|
|
" j = (i + 1) % len(better_solution)\n",
|
|
" city1 = points[better_solution[i]]\n",
|
|
" city2 = points[better_solution[j]]\n",
|
|
" ax2.plot([city1[0], city2[0]], [city1[1], city2[1]], 'g-')\n",
|
|
"\n",
|
|
"ax2.set_title(f'总距离: {better_distance:.2f}',fontsize = 20)\n",
|
|
"ax2.grid(True)\n",
|
|
"ax2.margins(0.13)\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
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|
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"display_name": "lead",
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|
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},
|
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"language_info": {
|
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"codemirror_mode": {
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"name": "ipython",
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},
|
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
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"version": "3.11.11"
|
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|
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