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# Copyright 2016, Gurobi Optimization, Inc.

# Solve a traveling salesman problem on a randomly generated set of
# points using lazy constraints.   The base MIP model only includes
# 'degree-2' constraints, requiring each node to have exactly
# two incident edges.  Solutions to this model may contain subtours -
# tours that don't visit every city.  The lazy constraint callback
# adds new constraints to cut them off.

import sys
import math
import random
from gurobipy import *

# Callback - use lazy constraints to eliminate sub-tours

def subtourelim(model, where):
    if where == GRB.Callback.MIPSOL:
        selected = []
        # make a list of edges selected in the solution
        for i in range(n):
            sol = model.cbGetSolution([model._vars[i,j] for j in range(n)])
            selected += [(i,j) for j in range(n) if sol[j] > 0.5]
        # find the shortest cycle in the selected edge list
        tour = subtour(selected)
        if len(tour) < n:
            # add a subtour elimination constraint
            expr = 0
            for i in range(len(tour)):
                for j in range(i+1, len(tour)):
                    expr += model._vars[tour[i], tour[j]]
            model.cbLazy(expr <= len(tour)-1)

# Euclidean distance between two points

def distance(points, i, j):
    dx = points[i][0] - points[j][0]
    dy = points[i][1] - points[j][1]
    return math.sqrt(dx*dx + dy*dy)

# Given a list of edges, finds the shortest subtour

def subtour(edges):
    visited = [False]*n
    cycles = []
    lengths = []
    selected = [[] for i in range(n)]
    for x,y in edges:
    while True:
        current = visited.index(False)
        thiscycle = [current]
        while True:
            visited[current] = True
            neighbors = [x for x in selected[current] if not visited[x]]
            if len(neighbors) == 0:
            current = neighbors[0]
        if sum(lengths) == n:
    return cycles[lengths.index(min(lengths))]

# Parse argument

if len(sys.argv) < 2:
    print('Usage: npoints')
n = int(sys.argv[1])

# Create n random points

points = []
for i in range(n):

m = Model()

# Create variables

vars = {}
for i in range(n):
    for j in range(i+1):
        vars[i,j] = m.addVar(obj=distance(points, i, j), vtype=GRB.BINARY,
        vars[j,i] = vars[i,j]

# Add degree-2 constraint, and forbid loops

for i in range(n):
    m.addConstr(quicksum(vars[i,j] for j in range(n)) == 2)
    vars[i,i].ub = 0

# Optimize model

m._vars = vars
m.params.LazyConstraints = 1

solution = m.getAttr('x', vars)
selected = [(i,j) for i in range(n) for j in range(n) if solution[i,j] > 0.5]
assert len(subtour(selected)) == n

print('Optimal tour: %s' % str(subtour(selected)))
print('Optimal cost: %g' % m.objVal)

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