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### tsp.py

#!/usr/bin/env python3.7

# Copyright 2020, Gurobi Optimization, LLC

# 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 itertools import combinations
import gurobipy as gp
from gurobipy import GRB

# Callback - use lazy constraints to eliminate sub-tours
def subtourelim(model, where):
if where == GRB.Callback.MIPSOL:
# make a list of edges selected in the solution
vals = model.cbGetSolution(model._vars)
selected = gp.tuplelist((i, j) for i, j in model._vars.keys()
if vals[i, j] > 0.5)
# find the shortest cycle in the selected edge list
tour = subtour(selected)
if len(tour) < n:
# add subtour elimination constr. for every pair of cities in tour
model.cbLazy(gp.quicksum(model._vars[i, j]
for i, j in combinations(tour, 2))
<= len(tour)-1)

# Given a tuplelist of edges, find the shortest subtour

def subtour(edges):
unvisited = list(range(n))
cycle = range(n+1)  # initial length has 1 more city
while unvisited:  # true if list is non-empty
thiscycle = []
neighbors = unvisited
while neighbors:
current = neighbors[0]
thiscycle.append(current)
unvisited.remove(current)
neighbors = [j for i, j in edges.select(current, '*')
if j in unvisited]
if len(cycle) > len(thiscycle):
cycle = thiscycle
return cycle

# Parse argument

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

# Create n random points

random.seed(1)
points = [(random.randint(0, 100), random.randint(0, 100)) for i in range(n)]

# Dictionary of Euclidean distance between each pair of points

dist = {(i, j):
math.sqrt(sum((points[i][k]-points[j][k])**2 for k in range(2)))
for i in range(n) for j in range(i)}

m = gp.Model()

# Create variables

vars = m.addVars(dist.keys(), obj=dist, vtype=GRB.BINARY, name='e')
for i, j in vars.keys():
vars[j, i] = vars[i, j]  # edge in opposite direction

# You could use Python looping constructs and m.addVar() to create
# these decision variables instead.  The following would be equivalent
# to the preceding m.addVars() call...
#
# vars = tupledict()
# for i,j in dist.keys():
#                        name='e[%d,%d]'%(i,j))

m.addConstrs(vars.sum(i, '*') == 2 for i in range(n))

# Using Python looping constructs, the preceding would be...
#
# for i in range(n):
#   m.addConstr(sum(vars[i,j] for j in range(n)) == 2)

# Optimize model

m._vars = vars
m.Params.lazyConstraints = 1
m.optimize(subtourelim)

vals = m.getAttr('x', vars)
selected = gp.tuplelist((i, j) for i, j in vals.keys() if vals[i, j] > 0.5)

tour = subtour(selected)
assert len(tour) == n

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


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