Add files via upload

ipynb/TSP.ipynb

@@ -213,7 +213,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114f26ac8>"
+       "<matplotlib.figure.Figure at 0x111d20940>"
       ]
      },
      "metadata": {},
@@ -263,14 +263,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "alltours: 9 cities ⇒ tour length 2450 (in 1.088 sec)\n"
+      "alltours: 9 cities ⇒ tour length 2450 (in 1.040 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11727a240>"
+       "<matplotlib.figure.Figure at 0x111f83a58>"
       ]
      },
      "metadata": {},
@@ -372,14 +372,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "alltours: 10 cities ⇒ tour length 2720 (in 1.521 sec)\n"
+      "alltours: 10 cities ⇒ tour length 2720 (in 1.470 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114f28ba8>"
+       "<matplotlib.figure.Figure at 0x111d2e198>"
       ]
      },
      "metadata": {},
@@ -472,14 +472,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nn: 200 cities ⇒ tour length 11477 (in 0.006 sec)\n"
+      "nn: 200 cities ⇒ tour length 11477 (in 0.005 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117298f28>"
+       "<matplotlib.figure.Figure at 0x111d2f0f0>"
       ]
      },
      "metadata": {},
@@ -499,14 +499,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nn: 1998 cities ⇒ tour length 33688 (in 0.601 sec)\n"
+      "nn: 1998 cities ⇒ tour length 33688 (in 0.523 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114f1be80>"
+       "<matplotlib.figure.Figure at 0x111f8f048>"
       ]
      },
      "metadata": {},
@@ -540,7 +540,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11929c198>"
+       "<matplotlib.figure.Figure at 0x1161261d0>"
       ]
      },
      "metadata": {},
@@ -606,7 +606,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1192829b0>"
+       "<matplotlib.figure.Figure at 0x112014470>"
       ]
      },
      "metadata": {},
@@ -626,14 +626,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rep_nn: 200 cities ⇒ tour length 10461 (in 0.159 sec)\n"
+      "rep_nn: 200 cities ⇒ tour length 10461 (in 0.143 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1141db0b8>"
+       "<matplotlib.figure.Figure at 0x110faa0f0>"
       ]
      },
      "metadata": {},
@@ -678,7 +678,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1178c7f98>"
+       "<matplotlib.figure.Figure at 0x11251fbe0>"
       ]
      },
      "metadata": {},
@@ -717,7 +717,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1178d00b8>"
+       "<matplotlib.figure.Figure at 0x1125f8908>"
       ]
      },
      "metadata": {},
@@ -815,7 +815,7 @@
    "source": [
     "# Improved Nearest Neighbor Algorithms\n",
     "\n",
-    "Here are three ways of improving nearest neighbor algorithms:"
+    "Here are two ways of improving nearest neighbor algorithms:"
    ]
   },
   {
@@ -828,10 +828,6 @@
     "    \"Improve the tour produced by nn_tsp.\"\n",
     "    return improve_tour(nn_tsp(cities))\n",
     "\n",
-    "def improve_rep_nn_tsp(cities, k=15):\n",
-    "    \"Improve the tour produced by rep_nn_tsp.\"\n",
-    "    return improve_tour(rep_nn_tsp(cities, k))\n",
-    "\n",
     "def rep_improve_nn_tsp(cities, k=5):\n",
     "    \"Run nn_tsp from k different starts, improve each tour; keep the best.\"\n",
     "    return shortest_tour(improve_tour(nn_tsp(cities, start)) \n",
@@ -854,14 +850,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nn: 200 cities ⇒ tour length 11477 (in 0.006 sec)\n"
+      "nn: 200 cities ⇒ tour length 11477 (in 0.005 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117a999b0>"
+       "<matplotlib.figure.Figure at 0x11260bdd8>"
       ]
      },
      "metadata": {},
@@ -881,14 +877,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "improve_nn: 200 cities ⇒ tour length 9523 (in 0.070 sec)\n"
+      "improve_nn: 200 cities ⇒ tour length 9523 (in 0.087 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1178c7d30>"
+       "<matplotlib.figure.Figure at 0x1125fd160>"
       ]
      },
      "metadata": {},
@@ -908,14 +904,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rep_improve_nn: 200 cities ⇒ tour length 9450 (in 0.333 sec)\n"
+      "rep_improve_nn: 200 cities ⇒ tour length 9450 (in 0.472 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1186c89e8>"
+       "<matplotlib.figure.Figure at 0x1155e9eb8>"
       ]
      },
      "metadata": {},
@@ -956,14 +952,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rep_improve_nn, 50: 200 cities ⇒ tour length 9264 (in 3.631 sec)\n"
+      "rep_improve_nn, 50: 200 cities ⇒ tour length 9264 (in 3.695 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117b4c358>"
+       "<matplotlib.figure.Figure at 0x11563d780>"
       ]
      },
      "metadata": {},
@@ -1037,7 +1033,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117acf940>"
+       "<matplotlib.figure.Figure at 0x112692320>"
       ]
      },
      "metadata": {},
@@ -1059,14 +1055,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rep_improve_nn, 80: 80 cities ⇒ tour length 13512 (in 0.887 sec)\n"
+      "rep_improve_nn, 80: 80 cities ⇒ tour length 13512 (in 0.890 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11796b198>"
+       "<matplotlib.figure.Figure at 0x112692da0>"
       ]
      },
      "metadata": {},
@@ -1120,14 +1116,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "nn: 1089 cities ⇒ tour length 52879 (in 0.166 sec)\n"
+      "nn: 1089 cities ⇒ tour length 52879 (in 0.164 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1179b4eb8>"
+       "<matplotlib.figure.Figure at 0x1125e5a58>"
       ]
      },
      "metadata": {},
@@ -1156,14 +1152,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rep_improve_nn, 3: 1089 cities ⇒ tour length 44877 (in 10.171 sec)\n"
+      "rep_improve_nn, 3: 1089 cities ⇒ tour length 44877 (in 10.007 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114b05860>"
+       "<matplotlib.figure.Figure at 0x11200c7f0>"
       ]
      },
      "metadata": {},
@@ -1281,7 +1277,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11781d160>"
+       "<matplotlib.figure.Figure at 0x114c440f0>"
       ]
      },
      "metadata": {},
@@ -1301,14 +1297,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "greedy: 1089 cities ⇒ tour length 46982 (in 0.569 sec)\n"
+      "greedy: 1089 cities ⇒ tour length 46982 (in 0.612 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11926b588>"
+       "<matplotlib.figure.Figure at 0x111f91cf8>"
       ]
      },
      "metadata": {},
@@ -1344,14 +1340,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "improve_greedy: 80 cities ⇒ tour length 14220 (in 0.015 sec)\n"
+      "improve_greedy: 80 cities ⇒ tour length 14220 (in 0.016 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117accc18>"
+       "<matplotlib.figure.Figure at 0x114c676a0>"
       ]
      },
      "metadata": {},
@@ -1371,14 +1367,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "improve_greedy: 1089 cities ⇒ tour length 43733 (in 2.996 sec)\n"
+      "improve_greedy: 1089 cities ⇒ tour length 43733 (in 2.967 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d9661d0>"
+       "<matplotlib.figure.Figure at 0x11a634208>"
       ]
      },
      "metadata": {},
@@ -1441,7 +1437,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114f1bf28>"
+       "<matplotlib.figure.Figure at 0x112903128>"
       ]
      },
      "metadata": {},
@@ -1451,7 +1447,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117ac36a0>"
+       "<matplotlib.figure.Figure at 0x111fe2470>"
       ]
      },
      "metadata": {},
@@ -1461,7 +1457,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c8f0860>"
+       "<matplotlib.figure.Figure at 0x11aada978>"
       ]
      },
      "metadata": {},
@@ -1471,7 +1467,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x118723a90>"
+       "<matplotlib.figure.Figure at 0x10b3b3b70>"
       ]
      },
      "metadata": {},
@@ -1481,7 +1477,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117993978>"
+       "<matplotlib.figure.Figure at 0x1125dc470>"
       ]
      },
      "metadata": {},
@@ -1491,7 +1487,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11781d128>"
+       "<matplotlib.figure.Figure at 0x11a41db38>"
       ]
      },
      "metadata": {},
@@ -1508,7 +1504,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117b4c208>"
+       "<matplotlib.figure.Figure at 0x11b8a1b70>"
       ]
      },
      "metadata": {},
@@ -1544,7 +1540,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1179057b8>"
+       "<matplotlib.figure.Figure at 0x11b8aab38>"
       ]
      },
      "metadata": {},
@@ -1576,7 +1572,7 @@
      "data": {
       "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c7ed0b8>"
+       "<matplotlib.figure.Figure at 0x11982e780>"
       ]
      },
      "metadata": {},
@@ -1604,7 +1600,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dc48a90>"
+       "<matplotlib.figure.Figure at 0x1192da2e8>"
       ]
      },
      "metadata": {},
@@ -1735,7 +1731,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11e442e48>"
+       "<matplotlib.figure.Figure at 0x1192da550>"
       ]
      },
      "metadata": {},
@@ -1755,14 +1751,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "improve_divide: 80 cities ⇒ tour length 14117 (in 0.138 sec)\n"
+      "improve_divide: 80 cities ⇒ tour length 14117 (in 0.109 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11c3bfa20>"
+       "<matplotlib.figure.Figure at 0x11b245780>"
       ]
      },
      "metadata": {},
@@ -1779,12 +1775,14 @@
    "source": [
     "Divide and conquer performs adequately, but not exceptionally. \n",
     "\n",
-    "# Shoulders of Giants: Minimum Spanning Tree Algorithm: `mst_tsp`\n",
+    "# Shoulders of Giants: Minimum Spanning Tree Algorithm\n",
     "\n",
     "\n",
-    "<a href=\"https://en.wikipedia.org/wiki/Joseph_Kruskal\">\n",
+    "| |\n",
-    "<img src=\"http://people.inf.elte.hu/hytruongson/Kruskal/J.Kruskal.jpg\">\n",
+    "|----|\n",
-    "<center>Joseph Kruskal (Wikipedia)</center></a>\n",
+    "| ![Joseph Kruskal (Wikipedia)](http://people.inf.elte.hu/hytruongson/Kruskal/J.Kruskal.jpg) |\n",
+    "| [Joseph Kruskal (Wikipedia)](https://en.wikipedia.org/wiki/Joseph_Kruskal) |\n",
+    "\n",
     "\n",
     "\n",
     "I hope you now believe that you could have come up with some ideas for solving the TSP, using the set of **strategies**. But even if you can't come up with something all on your own, you can follow the **Stand on the Shoulders of Giants Strategy**, also known as the **[Just Google it Strategy](http://bit.ly/XNGt2y)**, in which case you'll no doubt find a giant of a mathematician, [Joseph Kruskal](http://en.wikipedia.org/wiki/Joseph_Kruskal), who, in 1956, published [a paper](http://www.cmat.edu.uy/~marclan/TAG/Sellanes/Kruskal.pdf) that led to an algorithm that\n",
@@ -1869,7 +1867,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ce3c6d8>"
+       "<matplotlib.figure.Figure at 0x1197bf828>"
       ]
      },
      "metadata": {},
@@ -1951,7 +1949,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dac5b00>"
+       "<matplotlib.figure.Figure at 0x11b87e898>"
       ]
      },
      "metadata": {},
@@ -1971,14 +1969,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mst: 1089 cities ⇒ tour length 58059 (in 0.843 sec)\n"
+      "mst: 1089 cities ⇒ tour length 58059 (in 0.874 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d9661d0>"
+       "<matplotlib.figure.Figure at 0x111909860>"
       ]
      },
      "metadata": {},
@@ -1998,14 +1996,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "improve_mst: 1089 cities ⇒ tour length 44779 (in 5.871 sec)\n"
+      "improve_mst: 1089 cities ⇒ tour length 44779 (in 5.559 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117872fd0>"
+       "<matplotlib.figure.Figure at 0x11a3e7048>"
       ]
      },
      "metadata": {},
@@ -2043,12 +2041,16 @@
     "# Shoulders of Giants: Held-Karp Algorithm: `held_karp_tsp`\n",
     "\n",
     "\n",
-    "<a href=\"http://www.computerhistory.org/collections/catalog/102650390\"><img src=\"http://archive.computerhistory.org/resources/still-image/IBM/IBM_People/IBM.3_mathematicians_Held_Shareshian_Karp.ca1964.102650390.lg.jpg\">\n",
+    "|    |\n",
-    "<center>Held, Shareshian, Karp (Computer History Museum)</center></a>\n",
+    "|----|\n",
+    "| ![](http://archive.computerhistory.org/resources/still-image/IBM/IBM_People/IBM.3_mathematicians_Held_Shareshian_Karp.ca1964.102650390.lg.jpg) |\n",
+    "| [Held, Shareshian, Karp (Computer History Museum)](http://www.computerhistory.org/collections/catalog/102650390) |\n",
+    "\n",
+    "|    |\n",
+    "|----|\n",
+    "| ![](http://imgs.xkcd.com/comics/travelling_salesman_problem.png) |\n",
+    "| [xkcd 399](http://xkcd.com/399/) |\n",
     "\n",
-    "<a href=\"http://xkcd.com/399/\">\n",
-    "<img src=\"http://imgs.xkcd.com/comics/travelling_salesman_problem.png\">\n",
-    "<center>xkcd 399</center></a>\n",
     "\n",
     "Another algorithm that shows up with a literature search is the [Held-Karp Dynamic Programming Algorithm](http://en.wikipedia.org/wiki/Held%E2%80%93Karp_algorithm), named after giants [Michael Held](http://www.computerhistory.org/collections/catalog/102650390) and [Richard Karp](http://en.wikipedia.org/wiki/Richard_M._Karp). It is an algorithm for finding optimal tours, not approximate ones, so it is not appropriate for large *n*.  But even in its simplest form, without any programming tricks, it can go quite a bit further than `alltours_tsp`.  That is because `alltours_tsp` is O(*n*!), while the Held-Karp algorithm is only O(*n*<sup>2</sup> 2<sup>*n*</sup>).  How did Held and Karp achieve this speedup?  They noticed that `alltours_tsp` wastes a lot of time with permutations that can't possibly be optimal tours.  Here's the key idea:\n",
     "\n",
@@ -2130,14 +2132,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "alltours: 10 cities ⇒ tour length 2720 (in 1.690 sec)\n"
+      "alltours: 10 cities ⇒ tour length 2720 (in 1.624 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1141db0b8>"
+       "<matplotlib.figure.Figure at 0x1192daf98>"
       ]
      },
      "metadata": {},
@@ -2157,14 +2159,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "held_karp: 10 cities ⇒ tour length 2720 (in 0.033 sec)\n"
+      "held_karp: 10 cities ⇒ tour length 2720 (in 0.034 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ecce630>"
+       "<matplotlib.figure.Figure at 0x11a41e860>"
       ]
      },
      "metadata": {},
@@ -2191,14 +2193,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "held_karp: 18 cities ⇒ tour length 2771 (in 43.741 sec)\n"
+      "held_karp: 18 cities ⇒ tour length 2771 (in 45.137 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11dc59358>"
+       "<matplotlib.figure.Figure at 0x11260fc88>"
       ]
      },
      "metadata": {},
@@ -2257,14 +2259,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ensemble: 80 cities ⇒ tour length 13534 (in 0.191 sec)\n"
+      "ensemble: 80 cities ⇒ tour length 13534 (in 0.197 sec)\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ecef7f0>"
+       "<matplotlib.figure.Figure at 0x11a3e77b8>"
       ]
      },
      "metadata": {},
@@ -2288,7 +2290,7 @@
     "- **Divide and Conquer Strategy**: `divide_tsp`\n",
     "- **Ensemble Strategy**: `rep_nn_tsp`, `ensemble_tsp`\n",
     "- **Giant Shoulders Strategy**: `mst_tsp`, `held_karp_tsp`\n",
-    "- **Improvement Strategy**: `improve_nn_tsp`, `improve_greedy_tsp`, `improve_divide_tsp`, `improve_rep_nn_tsp`, `rep_improve_nn_tsp`, `improve_mst_tsp`\n",
+    "- **Improvement Strategy**: `improve_nn_tsp`, `improve_greedy_tsp`, `improve_divide_tsp`,  `rep_improve_nn_tsp`, `improve_mst_tsp`\n",
     "\n",
     "\n",
     "# Comparison of Algorithms: Benchmark Experiments\n",
@@ -2405,9 +2407,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114f1b198>"
+       "<matplotlib.figure.Figure at 0x1160fb320>"
       ]
      },
      "metadata": {},
@@ -2464,9 +2466,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117acc4a8>"
+       "<matplotlib.figure.Figure at 0x11a41ee10>"
       ]
      },
      "metadata": {},
@@ -2513,9 +2515,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11ce53080>"
+       "<matplotlib.figure.Figure at 0x112903c50>"
       ]
      },
      "metadata": {},
@@ -2525,20 +2527,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  #1  #2  #3  #4  #5  #6 | Algorithm\n",
+      "  #1  #2  #3  #4  #5 | Algorithm\n",
-      " --- --- --- --- --- --- | ---------\n",
+      " --- --- --- --- --- | ---------\n",
-      "  15   6   5   8   6   0 | improve_greedy\n",
+      "  16   7   7  10   0 | improve_greedy\n",
-      "  16  14   7   3   0   0 | rep_improve_nn\n",
+      "  19  15   6   0   0 | rep_improve_nn\n",
-      "   5   5  10  11   9   0 | improve_rep_nn\n",
+      "   1   1   1   4  33 | improve_divide\n",
-      "   1   1   0   1   4  33 | improve_divide\n",
+      "   2  13  14   8   3 | improve_nn\n",
-      "   1  12   9   7   8   3 | improve_nn\n",
+      "   2   4  12  18   4 | improve_mst\n"
-      "   2   2   9  10  13   4 | improve_mst\n"
      ]
     }
    ],
    "source": [
-    "improved = [improve_greedy_tsp, rep_improve_nn_tsp, improve_rep_nn_tsp, \n",
+    "improved = [improve_greedy_tsp, rep_improve_nn_tsp, improve_divide_tsp, improve_nn_tsp, improve_mst_tsp]\n",
-    "            improve_divide_tsp, improve_nn_tsp, improve_mst_tsp, ]\n",
     "\n",
     "compare(improved)"
    ]
@@ -2561,9 +2561,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x13b050b00>"
+       "<matplotlib.figure.Figure at 0x138258978>"
       ]
      },
      "metadata": {},
@@ -2607,9 +2607,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x13b616080>"
+       "<matplotlib.figure.Figure at 0x1384cef28>"
       ]
      },
      "metadata": {},
@@ -2651,9 +2651,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ea92cc0>"
+       "<matplotlib.figure.Figure at 0x10c400e48>"
       ]
      },
      "metadata": {},
@@ -2671,9 +2671,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x114b05860>"
+       "<matplotlib.figure.Figure at 0x10a7fbdd8>"
       ]
      },
      "metadata": {},