Merge branch 'give_up_on_10k' of tarfeef101/cs486 into master

a1/README.txt

@@ -0,0 +1 @@
+To run the solvers, simply run "python3 sudoku.py", "python3 tsp.py" and "python3 tsp_dumb.py"

a1/sudoku.py

@@ -1,19 +1,7 @@
-import sys
 import copy
 from contextlib import suppress
 from heapq import heappush, heappop
-
-board = sys.argv[1]
-algotype = sys.argv[2]
-
-# import board
-with open(board) as file:
-    board = file.read().splitlines()
-    board = board[:-1]
-
-# convert to list of list of ints
-for l in board:
-    board[board.index(l)] = list(map(lambda x: int(x), l.split()))
+import matplotlib.pyplot as plt
 
 # return a board that is like the board b, but has domains for each element of b (always 1-9)
 def genDomains(b):
@@ -59,7 +47,10 @@ def naive(start):
     assumptions = []
                     
     if(len(unassigned) == 0):
-        return True
+        return (working, 0)
+        
+    # count assignments
+    count = 0
     
     # while there are unassigned vars, keep going
     while(len(unassigned)):
@@ -69,8 +60,18 @@ def naive(start):
         # iterate over all values in the domain list
         while solution[index[0]][index[1]]:
             i = solution[index[0]][index[1]].pop()
+            count += 1
+            # took too long
+            if (count >= 10000):
+                print("took too long")
+                return 10000
+
             # check if this part of the domain(solution) is valid
             if (valid(working, index[0], index[1], i)):
+                #count += 1
+                #if (count >= 10000):
+                #  print("took too long")
+                #  return False
                 solution[index[0]][index[1]].append(i) # keep in the domain
                 working[index[0]][index[1]] = i
                 assumptions.append(index)
@@ -91,9 +92,9 @@ def naive(start):
     
     
     # if we exit without assigning everything, we should have failed
-    if (unassigned): return False
+    if (unassigned): return 10000
 
-    return working
+    return count
 
 
 # returns a board (domains) where inferences are made for the cell at row, col
@@ -130,42 +131,63 @@ def gen2Domains(b):
 
 
 # recursive solver for forward-checking
-def solve(working, domains, unassigned):
+def solve(working, domains, unassigned, count):
     if (not unassigned):
-        return working
-    
+        return (True, count)
+
     index = unassigned.pop()
     
     # for every value in the domain, check if using it works. if all fail, backtrack.
     for i in domains[index[0]][index[1]]:
         working[index[0]][index[1]] = i
         newdomains = infer(domains, working, index[0], index[1], i)
-        result = solve(working, newdomains, copy.deepcopy(unassigned))
-        if (result):
+        domains[index[0]][index[1]].remove(i)
+        count += 1
+        # took too long
+        if (count >= 10000):
+          print("took too long")
+          return (True, 10000)
+
+        # check for invalidated nodes (empty domains)
+        flag = True
+        result = False
+        for i in range(0, 9):
+          for j in range(0, 9):
+            if (len(newdomains[i][j]) <= 0):
+              flag = False
+        
+        if (flag): result = solve(working, newdomains, copy.deepcopy(unassigned), count)
+        if (not result):
+          return (False, count)
+        if (result[0]):
             return result
         else:
-            continue
+            #domains[index[0]][index[1]].remove(i)
+            count = result[1]
     
-    return False
+    return (False, count)
 
 # forward checking solver
 def forward(start):
     working = copy.deepcopy(start) # this is only "filled in values" and 0s
-    domains = gen2Domains(start)
+    domains = gen2Domains(working)
     # unassigned will be a list of positions we have to fill
     unassigned = []
     for i in range(0, 9):
         for j in range(0, 9):
             if (len(domains[i][j]) == 9):
                 unassigned.append((i, j))
-    
+   
     # forward-checking on pre-assigned values
     for i in range(0, 9):
         for j in range(0, 9):
             if (working[i][j] != 0):
                 domains = infer(domains, working, i, j, working[i][j])
             
-    return solve(working, domains, unassigned)
+    result = solve(working, domains, unassigned, 0)
+    return result[1]
+    #if (result[0]): return result[1]
+    #else: return 10000
 
 
 # returns size of domain for a given index
@@ -258,9 +280,9 @@ def genVal(domains, working, unassigned):
 
 
 # recursive solver that uses heuristics to decide what node to explore
-def solveh(working, domains, unassigned):
+def solveh(working, domains, unassigned, count):
     if (not unassigned):
-        return working
+        return (True, count)
     
     # while there are unassigned values keep trying
     while(unassigned):
@@ -270,26 +292,35 @@ def solveh(working, domains, unassigned):
         val = nextThing[1]
         working[index[0]][index[1]] = val
         unassigned.remove(index)
-    
+        
         # check for invalidated nodes (empty domain)
         flag = True
         result = False
         newdomains = infer(domains, working, index[0], index[1], val)
         for i in range(0, 9):
             for j in range(0, 9):
-                if (not domains[i][j]):
+                if (not newdomains[i][j]):
                     flag = False
         
+        count += 1
+        # took too long
+        if (count >= 10000):
+          print("took too long")
+          return (False, count)
+
         # success! recurse
-        if (flag): result = solveh(working, newdomains, copy.deepcopy(unassigned))
-        if (result):
+        if (flag): result = solveh(working, newdomains, copy.deepcopy(unassigned), count)
+        if (not result): pass
+        elif (result[0]):
             return result
         elif (len(domains[index[0]][index[1]]) > 1): # remove from domain, keep going
             working[index[0]][index[1]] = 0
             domains[index[0]][index[1]].remove(val)
             unassigned.append(index)
+            if (flag): count = result[1]
         else: # no values worked :( return false
-            return False
+            if (flag): return (False, result[1])
+            return (False, count)
 
 
 # forward checking solver with heuristics
@@ -309,25 +340,49 @@ def heuristic(start):
             if (working[i][j] != 0):
                 domains = infer(domains, working, i, j, working[i][j])
             
-    return solveh(working, domains, unassigned)
+    result = solveh(working, domains, unassigned, 0)
+    if (result[0]): return result[1]
+    else: return 10000
 
 
 def main():
-    print("###########")
-    print(*board, sep='\n')
-    print("##########")
-
-    if (algotype == str(0)):
-      result = naive(board)
-    elif (algotype == str(1)):
-      result = forward(board)
-    elif (algotype == str(2)):
-      result = heuristic(board)
-    else:
-      print("No valid algorithm selected. RIP.")
+    plt.ioff()
+    plt.switch_backend('agg')
+    averages = []
+    bverages = []
+    cverages = []
+    
+    for i in range(1, 72):
+        avgA = 0
+        avgB = 0
+        avgC = 0
+        for j in range(1, 11):
+            filepath = "sudoku_problems/" + str(i) + "/" + str(j) + ".sd"
+            
+            # import board
+            with open(filepath) as file:
+                board = file.read().splitlines()
+                board = board[:-1]
 
-    print("###########")
-    print(*result, sep='\n')
-    print("##########")
+            # convert to list of list of ints
+            for l in board:
+                board[board.index(l)] = list(map(lambda x: int(x), l.split()))
+            
+            avgA += naive(copy.deepcopy(board));print(i, j)
+            avgB += forward(copy.deepcopy(board));print(i, j)
+            avgC += heuristic(copy.deepcopy(board));print(i, j)
+            
+        averages.append(avgA / 10.0)
+        bverages.append(avgB / 10.0)
+        cverages.append(avgC / 10.0)
+        
+    figure, axes = plt.subplots(1, 1, True)
+    axes.plot(range(1, 72), averages, label='Naive Algorithm')
+    axes.plot(range(1, 72), bverages, label='Forward-Checking Algorithm')
+    axes.plot(range(1, 72), cverages, label='Heuristics')
+    axes.legend()
+    plt.xlabel("Number of Initial Valued Filled In")
+    plt.ylabel("Average Number of Variable Assignments in 10 Runs")
+    plt.savefig("graph.pdf")
     
 main()

a1/tsp.py

@@ -0,0 +1,132 @@
+import sys
+import copy
+import math
+from heapq import heappush, heappop
+from random import randint
+import matplotlib.pyplot as plt
+
+distances = []
+n = 0
+
+# returns hueristic value
+def heuristic(source, target, dist):
+    #return 0
+    for i in range(0, min(n * 2, n ** 2)):
+        if (dist <= distances[i]):
+            return 0
+    return dist
+
+
+# returns weight(distance) between 2 vertices
+def weight(city1, city2):
+    return math.sqrt((city1[1] - city2[1]) ** 2 + (city1[2] - city2[2]) ** 2)
+
+
+# returns if going to city i from curnode is okay
+def valid(curnode, cities, i):
+    # can't revisit a city
+    if (i in curnode[2]):
+        return False
+    # if curnode hasn't visited everything else and we are going to the gaol
+    elif (len(curnode[2]) != (len(cities) - 1) and i == (len(cities) - 1)):
+        return False
+    else:
+        return True
+
+
+# returns the count of child states. updates frontier
+def explore(curnode, frontier, cities):
+    children = 0
+    
+    for i in range(1, len(cities)):
+        # can we go here?
+        if (valid(curnode, cities, i)):
+            children += 1
+            # add to frontier
+            dist = weight(curnode[1], cities[i])
+            metric = dist + heuristic(curnode[1], cities[i], dist)
+            heappush(frontier, [curnode[0] + metric, cities[i], copy.deepcopy(curnode[2])])
+    
+    return children
+
+
+# A* solver for TSP
+def solve(frontier, cities):
+    # number of child states
+    newnodes = 0
+    curnode = heappop(frontier)
+    
+    # while we are not on the goal node
+    while (curnode[1] != cities[-1]):
+        newnodes += explore(curnode, frontier, cities)
+        curnode = heappop(frontier)
+        curnode[2].append(cities.index(curnode[1]))
+        
+    return newnodes + 1
+
+
+def tsp(filename):
+    # import map
+    #prob = "instance_10.txt"
+    prob = filename
+    with open(prob) as file:
+        prob = file.read().splitlines()
+
+    n = int(prob[0])
+    cities = prob[1:]
+    counter = 0
+    # convert to list of list of ints
+    for l in cities:
+        temp = l.split()
+        temp[1] = int(temp[1])
+        temp[2] = int(temp[2])
+        temp.append(counter)
+        counter += 1
+        cities[cities.index(l)] = temp
+        
+    distances = []
+    
+    for i in range(0, len(cities) - 1):
+        for j in range(i, len(cities)):
+            dist = weight(cities[i], cities[j])
+            distances.append(dist)
+                
+    distances.sort()
+    
+    # add in goal state
+    cities.append(copy.deepcopy(cities[0]))
+    cities[-1][0] = "Goal"
+    
+    # create frontier
+    frontier = []
+    # add start state, list of visited, and distancesofar to frontier
+    heappush(frontier, [0, cities[0], [0]])
+
+    result = solve(frontier, cities)
+    print(result)
+    return result
+
+
+def main():
+    global distances
+    global n
+    plt.ioff()
+    plt.switch_backend('agg')
+    averages = []
+    
+    for i in range(1, 12):
+        average = 0
+        for j in range(1, 11):
+            filepath = "tsp_problems/" + str(i) + "/instance_" + str(j) + ".txt"
+            average += tsp(filepath)
+            
+        averages.append(average / 10.0)
+    
+    figure, axes = plt.subplots(1, 1, True)
+    axes.semilogy(range(1, 12), averages, label='TSP Solver (Heuristic)')
+    axes.legend()
+    plt.xlabel("Number of Cities")
+    plt.ylabel("Average Number of Nodes Generated in 10 Runs")
+    plt.savefig("tsph.pdf")
+    
+main()

a1/tsp_dumb.py

@@ -0,0 +1,132 @@
+import sys
+import copy
+import math
+from heapq import heappush, heappop
+from random import randint
+import matplotlib.pyplot as plt
+
+#distances = []
+n = 0
+
+# returns hueristic value
+def heuristic(source, target, dist):
+    return 0
+    #for i in range(0, min(n * 2, n ** 2)):
+    #    if (dist <= distances[i]):
+    #        return 0
+    #return dist
+
+
+# returns weight(distance) between 2 vertices
+def weight(city1, city2):
+    return math.sqrt((city1[1] - city2[1]) ** 2 + (city1[2] - city2[2]) ** 2)
+
+
+# returns if going to city i from curnode is okay
+def valid(curnode, cities, i):
+    # can't revisit a city
+    if (i in curnode[2]):
+        return False
+    # if curnode hasn't visited everything else and we are going to the gaol
+    elif (len(curnode[2]) != (len(cities) - 1) and i == (len(cities) - 1)):
+        return False
+    else:
+        return True
+
+
+# returns the count of child states. updates frontier
+def explore(curnode, frontier, cities):
+    children = 0
+    
+    for i in range(1, len(cities)):
+        # can we go here?
+        if (valid(curnode, cities, i)):
+            children += 1
+            # add to frontier
+            dist = weight(curnode[1], cities[i])
+            metric = dist + heuristic(curnode[1], cities[i], dist)
+            heappush(frontier, [curnode[0] + metric, cities[i], copy.deepcopy(curnode[2])])
+    
+    return children
+
+
+# A* solver for TSP
+def solve(frontier, cities):
+    # number of child states
+    newnodes = 0
+    curnode = heappop(frontier)
+    
+    # while we are not on the goal node
+    while (curnode[1] != cities[-1]):
+        newnodes += explore(curnode, frontier, cities)
+        curnode = heappop(frontier)
+        curnode[2].append(cities.index(curnode[1]))
+        
+    return newnodes + 1
+
+
+def tsp(filename):
+    # import map
+    #prob = "instance_10.txt"
+    prob = filename
+    with open(prob) as file:
+        prob = file.read().splitlines()
+
+    n = int(prob[0])
+    cities = prob[1:]
+    counter = 0
+    # convert to list of list of ints
+    for l in cities:
+        temp = l.split()
+        temp[1] = int(temp[1])
+        temp[2] = int(temp[2])
+        temp.append(counter)
+        counter += 1
+        cities[cities.index(l)] = temp
+        
+    #distances = []
+    
+    #for i in range(0, len(cities) - 1):
+    #    for j in range(i, len(cities)):
+    #        dist = weight(cities[i], cities[j])
+    #        distances.append(dist)
+                
+    #distances.sort()
+    
+    # add in goal state
+    cities.append(copy.deepcopy(cities[0]))
+    cities[-1][0] = "Goal"
+    
+    # create frontier
+    frontier = []
+    # add start state, list of visited, and distancesofar to frontier
+    heappush(frontier, [0, cities[0], [0]])
+
+    result = solve(frontier, cities)
+    print(result)
+    return result
+
+
+def main():
+    #global distances
+    global n
+    plt.ioff()
+    plt.switch_backend('agg')
+    averages = []
+    
+    for i in range(1, 12):
+        average = 0
+        for j in range(1, 11):
+            filepath = "tsp_problems/" + str(i) + "/instance_" + str(j) + ".txt"
+            average += tsp(filepath)
+            
+        averages.append(average / 10.0)
+    
+    figure, axes = plt.subplots(1, 1, True)
+    axes.semilogy(range(1, 12), averages, label='TSP Solver (Heuristic)')
+    axes.legend()
+    plt.xlabel("Number of Cities")
+    plt.ylabel("Average Number of Nodes Generated in 10 Runs")
+    plt.savefig("tspdumb.pdf")
+    
+main()

a1/tsp_problems/1/instance_1.txt

@@ -0,0 +1,2 @@
+1
+A 94 66

a1/tsp_problems/1/instance_10.txt

@@ -0,0 +1,2 @@
+1
+A 78 34

a1/tsp_problems/1/instance_2.txt

@@ -0,0 +1,2 @@
+1
+A 30 26

a1/tsp_problems/1/instance_3.txt

@@ -0,0 +1,2 @@
+1
+A 84 22

a1/tsp_problems/1/instance_4.txt

@@ -0,0 +1,2 @@
+1
+A 48 75

a1/tsp_problems/1/instance_5.txt

@@ -0,0 +1,2 @@
+1
+A 32 86

a1/tsp_problems/1/instance_6.txt

@@ -0,0 +1,2 @@
+1
+A 55 23

a1/tsp_problems/1/instance_7.txt

@@ -0,0 +1,2 @@
+1
+A 37 91

a1/tsp_problems/1/instance_8.txt

@@ -0,0 +1,2 @@
+1
+A 71 47

a1/tsp_problems/1/instance_9.txt

@@ -0,0 +1,2 @@
+1
+A 10 49

a1/tsp_problems/10/instance_1.txt

@@ -0,0 +1,11 @@
+10
+A 66 62
+B 89 26
+C 20 27
+D 17 56
+E 2 74
+F 37 40
+G 41 24
+H 55 72
+I 32 12
+J 94 22

a1/tsp_problems/10/instance_10.txt

@@ -0,0 +1,11 @@
+10
+A 22 91
+B 69 99
+C 32 27
+D 52 61
+E 36 56
+F 37 8
+G 54 69
+H 88 80
+I 32 51
+J 47 73

a1/tsp_problems/10/instance_2.txt

@@ -0,0 +1,11 @@
+10
+A 28 36
+B 30 61
+C 30 20
+D 60 38
+E 20 21
+F 64 54
+G 9 91
+H 31 80
+I 68 98
+J 87 50

a1/tsp_problems/10/instance_3.txt

@@ -0,0 +1,11 @@
+10
+A 42 71
+B 67 22
+C 71 39
+D 32 70
+E 88 19
+F 67 22
+G 24 11
+H 87 11
+I 85 1
+J 91 65

a1/tsp_problems/10/instance_4.txt

@@ -0,0 +1,11 @@
+10
+A 65 85
+B 14 87
+C 95 35
+D 5 46
+E 83 22
+F 24 83
+G 7 51
+H 7 58
+I 58 32
+J 50 89

a1/tsp_problems/10/instance_5.txt

@@ -0,0 +1,11 @@
+10
+A 85 94
+B 62 33
+C 82 38
+D 88 37
+E 22 47
+F 63 7
+G 88 94
+H 1 23
+I 10 16
+J 46 53

a1/tsp_problems/10/instance_6.txt

@@ -0,0 +1,11 @@
+10
+A 94 43
+B 12 44
+C 67 97
+D 23 36
+E 83 92
+F 7 25
+G 46 72
+H 91 89
+I 48 65
+J 81 15

a1/tsp_problems/10/instance_7.txt

@@ -0,0 +1,11 @@
+10
+A 2 97
+B 8 46
+C 28 26
+D 46 14
+E 48 8
+F 92 8
+G 97 68
+H 99 95
+I 86 77
+J 73 75

a1/tsp_problems/10/instance_8.txt

@@ -0,0 +1,11 @@
+10
+A 89 81
+B 12 97
+C 22 51
+D 74 37
+E 62 19
+F 21 36
+G 98 34
+H 65 17
+I 27 43
+J 56 25

a1/tsp_problems/10/instance_9.txt

@@ -0,0 +1,11 @@
+10
+A 32 16
+B 83 84
+C 81 84
+D 60 23
+E 30 76
+F 73 99
+G 99 51
+H 29 19
+I 43 61
+J 23 70

a1/tsp_problems/11/instance_1.txt

@@ -0,0 +1,12 @@
+11
+A 9 50
+B 71 1
+C 69 34
+D 56 10
+E 84 62
+F 71 62
+G 17 99
+H 74 90
+I 17 77
+J 56 27
+K 41 50

a1/tsp_problems/11/instance_10.txt

@@ -0,0 +1,12 @@
+11
+A 7 13
+B 68 55
+C 28 11
+D 42 67
+E 3 61
+F 94 63
+G 31 47
+H 98 19
+I 38 26
+J 6 74
+K 40 31

a1/tsp_problems/11/instance_2.txt

@@ -0,0 +1,12 @@
+11
+A 50 71
+B 20 55
+C 62 30
+D 53 86
+E 62 79
+F 43 99
+G 24 50
+H 42 76
+I 96 46
+J 51 91
+K 75 17

a1/tsp_problems/11/instance_3.txt

@@ -0,0 +1,12 @@
+11
+A 70 8
+B 95 96
+C 42 99
+D 65 27
+E 49 85
+F 29 81
+G 49 62
+H 87 40
+I 83 94
+J 68 71
+K 44 40

a1/tsp_problems/11/instance_4.txt

@@ -0,0 +1,12 @@
+11
+A 51 87
+B 99 24
+C 8 19
+D 10 83
+E 87 34
+F 79 64
+G 9 79
+H 10 65
+I 15 88
+J 96 76
+K 34 19

a1/tsp_problems/11/instance_5.txt

@@ -0,0 +1,12 @@
+11
+A 54 24
+B 76 77
+C 96 73
+D 40 3
+E 28 60
+F 36 41
+G 96 50
+H 41 60
+I 2 74
+J 42 4
+K 21 28

a1/tsp_problems/11/instance_6.txt

@@ -0,0 +1,12 @@
+11
+A 24 43
+B 47 15
+C 85 17
+D 67 7
+E 49 66
+F 20 56
+G 87 50
+H 58 94
+I 31 24
+J 92 41
+K 14 98

a1/tsp_problems/11/instance_7.txt

@@ -0,0 +1,12 @@
+11
+A 22 55
+B 23 14
+C 70 44
+D 85 30
+E 31 98
+F 16 84
+G 33 13
+H 62 6
+I 46 28
+J 45 14
+K 84 31

a1/tsp_problems/11/instance_8.txt

@@ -0,0 +1,12 @@
+11
+A 70 49
+B 79 68
+C 52 80
+D 68 5
+E 35 76
+F 94 8
+G 74 71
+H 25 14
+I 66 16
+J 14 10
+K 8 54

a1/tsp_problems/11/instance_9.txt

@@ -0,0 +1,12 @@
+11
+A 78 17
+B 25 24
+C 96 9
+D 49 7
+E 8 12
+F 18 55
+G 84 38
+H 93 51
+I 2 38
+J 56 23
+K 78 57

a1/tsp_problems/12/instance_1.txt

@@ -0,0 +1,13 @@
+12
+A 95 10
+B 70 2
+C 71 25
+D 42 51
+E 84 7
+F 87 39
+G 79 45
+H 85 74
+I 93 58
+J 21 72
+K 99 20
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a1/tsp_problems/12/instance_10.txt

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a1/tsp_problems/12/instance_2.txt

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a1/tsp_problems/12/instance_3.txt

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a1/tsp_problems/12/instance_4.txt

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a1/tsp_problems/12/instance_5.txt

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a1/tsp_problems/12/instance_6.txt

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a1/tsp_problems/12/instance_7.txt

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a1/tsp_problems/12/instance_8.txt

@@ -0,0 +1,13 @@
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a1/tsp_problems/12/instance_9.txt

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a1/tsp_problems/13/instance_1.txt

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a1/tsp_problems/13/instance_10.txt

@@ -0,0 +1,14 @@
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a1/tsp_problems/13/instance_2.txt

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a1/tsp_problems/13/instance_3.txt

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a1/tsp_problems/13/instance_4.txt

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a1/tsp_problems/13/instance_5.txt

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a1/tsp_problems/13/instance_6.txt

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a1/tsp_problems/13/instance_7.txt

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a1/tsp_problems/13/instance_8.txt

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a1/tsp_problems/13/instance_9.txt

@@ -0,0 +1,14 @@
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a1/tsp_problems/14/instance_1.txt

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a1/tsp_problems/14/instance_10.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_2.txt

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a1/tsp_problems/14/instance_3.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_4.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_5.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_6.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_7.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_8.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/14/instance_9.txt

@@ -0,0 +1,15 @@
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a1/tsp_problems/15/instance_1.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_10.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_2.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_3.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_4.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_5.txt

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a1/tsp_problems/15/instance_6.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_7.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_8.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/15/instance_9.txt

@@ -0,0 +1,16 @@
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a1/tsp_problems/16/instance_1.txt

@@ -0,0 +1,17 @@
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a1/tsp_problems/16/instance_10.txt

@@ -0,0 +1,17 @@
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a1/tsp_problems/16/instance_2.txt

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a1/tsp_problems/16/instance_3.txt

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a1/tsp_problems/16/instance_4.txt

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a1/tsp_problems/16/instance_5.txt

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a1/tsp_problems/16/instance_6.txt

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a1/tsp_problems/16/instance_7.txt

@@ -0,0 +1,17 @@
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a1/tsp_problems/16/instance_8.txt

@@ -0,0 +1,17 @@
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a1/tsp_problems/16/instance_9.txt

@@ -0,0 +1,17 @@
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a1/tsp_problems/2/instance_1.txt

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a1/tsp_problems/2/instance_10.txt

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a1/tsp_problems/2/instance_2.txt

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a1/tsp_problems/2/instance_3.txt

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a1/tsp_problems/2/instance_4.txt

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a1/tsp_problems/2/instance_5.txt

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a1/tsp_problems/2/instance_6.txt

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a1/tsp_problems/2/instance_7.txt

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a1/tsp_problems/2/instance_8.txt

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a1/tsp_problems/2/instance_9.txt

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a1/tsp_problems/3/instance_1.txt

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a1/tsp_problems/3/instance_10.txt

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a1/tsp_problems/3/instance_2.txt

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a1/tsp_problems/3/instance_3.txt

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a1/tsp_problems/3/instance_4.txt

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