53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
#!/usr/bin/env python
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# @(#) $Jeannot: test1.py,v 1.11 2005/01/06 21:22:39 js Exp $
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# Import PuLP modeler functions
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from pulp import *
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# A new LP problem
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prob = LpProblem("test1", LpMinimize)
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# Variables
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# 0 <= x <= 4
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x = LpVariable("x", 0, 4)
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# -1 <= y <= 1
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y = LpVariable("y", -1, 1)
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# 0 <= z
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z = LpVariable("z", 0)
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# Use None for +/- Infinity, i.e. z <= 0 -> LpVariable("z", None, 0)
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# Objective
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prob += x + 4*y + 9*z, "obj"
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# (the name at the end is facultative)
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# Constraints
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prob += x+y <= 5, "c1"
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prob += x+z >= 10, "c2"
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prob += -y+z == 7, "c3"
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# (the names at the end are facultative)
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# Write the problem as an LP file
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prob.writeLP("test1.lp")
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# Solve the problem using the default solver
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prob.solve()
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# Use prob.solve(GLPK()) instead to choose GLPK as the solver
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# Use GLPK(msg = 0) to suppress GLPK messages
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# If GLPK is not in your path and you lack the pulpGLPK module,
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# replace GLPK() with GLPK("/path/")
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# Where /path/ is the path to glpsol (excluding glpsol itself).
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# If you want to use CPLEX, use CPLEX() instead of GLPK().
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# If you want to use XPRESS, use XPRESS() instead of GLPK().
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# If you want to use COIN, use COIN() instead of GLPK(). In this last case,
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# two paths may be provided (one to clp, one to cbc).
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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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