49 lines
1.0 KiB
Python
49 lines
1.0 KiB
Python
#!/usr/bin/env python
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# @(#) $Jeannot: test2.py,v 1.16 2004/03/20 17:06:54 js Exp $
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# This example is a PuLP rendition of the todd.mod problem included in the GLPK
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# 4.4 distribution. It's a hard knapsack problem.
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# Import PuLP modeler functions
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from pulp import *
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# Import math functions
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from math import *
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# A new LP problem
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prob = LpProblem("test2", LpMaximize)
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# Parameters
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# Size of the problem
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n = 15
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k = floor(log(n)/log(2));
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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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# A vector of weights
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a = [pow(2,k + n + 1) + pow(2,k + n + 1 - j) + 1 for j in range(1,n+1)]
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# The maximum weight
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b = 0.5 * floor(sum(a))
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# The total weight
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weight = lpDot(a, x)
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# Objective
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prob += weight
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# Constraint
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prob += weight <= b
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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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