54 lines
1.2 KiB
Python
54 lines
1.2 KiB
Python
#!/usr/bin/env python
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# @(#) $Jeannot: test5.py,v 1.2 2004/03/20 17:06:54 js Exp $
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# Market splitting problems from:
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# G. Cornuejols, M. Dawande, A class of hard small 0-1 programs, 1998.
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# With m>=4, these problems are often *very* difficult.
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# Import PuLP modeler functions
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from pulp import *
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# Import random number generation functions
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from random import *
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# A new LP problem
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prob = LpProblem("test5", LpMinimize)
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# Parameters
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# Number of constraints
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m = 3
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# Size of the integers involved
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D = 100
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# Number of variables
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n = 10*(m-1)
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# A vector of n binary variables
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x = LpVariable.matrix("x", list(range(n)), 0, 1, LpInteger)
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# Slacks
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s = LpVariable.matrix("s", list(range(m)), 0)
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w = LpVariable.matrix("w", list(range(m)), 0)
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# Objective
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prob += lpSum(s) + lpSum(w)
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# Constraints
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d = [[randint(0,D) for i in range(n)] for j in range(m)]
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for j in range(m):
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prob += lpDot(d[j], x) + s[j] - w[j] == lpSum(d[j])/2
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# Resolution
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prob.solve()
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# Print the status of the solved LP
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print("Status:", LpStatus[prob.status])
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# Print the value of the variables at the optimum
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for v in prob.variables():
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print(v.name, "=", v.varValue)
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# Print the value of the objective
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print("objective=", value(prob.objective))
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