154 lines
5.0 KiB
Python
154 lines
5.0 KiB
Python
# Copyright 2010-2011 Google
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""pywraplp example file."""
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from google.apputils import app
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import gflags
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from linear_solver import pywraplp
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FLAGS = gflags.FLAGS
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class PyWrapLPExamples(object):
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"""Class that contains a collection of LP examples."""
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def RunFirstLinearExample(self, mode):
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"""Minimal Linear Example."""
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solver = pywraplp.Solver('RunFirstLinearExample', mode)
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infinity = solver.infinity()
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x1 = solver.NumVar(0.0, infinity, 'x1')
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x2 = solver.NumVar(0.0, infinity, 'x2')
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x3 = solver.NumVar(0.0, infinity, 'x3')
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print 'number of variables = ', solver.NumVariables()
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solver.AddObjectiveTerm(x1, 10)
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solver.AddObjectiveTerm(x2, 6)
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solver.AddObjectiveTerm(x3, 4)
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solver.SetMaximization()
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c0 = solver.Constraint(-infinity, 100.0)
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c0.AddTerm(x1, 1)
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c0.AddTerm(x2, 1)
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c0.AddTerm(x3, 1)
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c1 = solver.Constraint(-infinity, 600.0)
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c1.AddTerm(x1, 10)
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c1.AddTerm(x2, 4)
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c1.AddTerm(x3, 5)
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c2 = solver.Constraint(-infinity, 300.0)
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c2.AddTerm(x1, 2)
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c2.AddTerm(x2, 2)
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c2.AddTerm(x3, 6)
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print 'number of constraints = ', solver.NumConstraints()
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# The problem has an optimal solution.
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solver.Solve()
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print 'optimal objective value = ', solver.objective_value()
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print 'x1 = ', x1.solution_value(), ', reduced_cost = ', x1.reduced_cost()
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print 'x2 = ', x2.solution_value(), ', reduced_cost = ', x2.reduced_cost()
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print 'x3 = ', x3.solution_value(), ', reduced_cost = ', x3.reduced_cost()
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print 'c0 dual value = ', c0.dual_value()
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print 'c0 activity = ', c0.activity()
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print 'c1 dual value = ', c1.dual_value()
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print 'c1 activity = ', c1.activity()
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print 'c2 dual value = ', c2.dual_value()
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print 'c2 activity = ', c2.activity()
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def RunAllFirstLinearExample(self):
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self.RunFirstLinearExample(pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)
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self.RunFirstLinearExample(pywraplp.Solver.CLP_LINEAR_PROGRAMMING)
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def RunFirstLinearExampleNewAPI(self, mode):
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"""Minimal LP Example with New API."""
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solver = pywraplp.Solver('RunFirstLinearExample', mode)
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infinity = solver.infinity()
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x1 = solver.NumVar(0.0, infinity, 'x1')
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x2 = solver.NumVar(0.0, infinity, 'x2')
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x3 = solver.NumVar(0.0, infinity, 'x3')
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print 'number of variables = ', solver.NumVariables()
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solver.Maximize(10 * x1 + 6 * x2 + 4 * x3)
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c0 = solver.Add(x1 + x2 + x3 <= 100.0)
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c1 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600)
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c2 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)
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print 'number of constraints = ', solver.NumConstraints()
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# The problem has an optimal solution.
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solver.Solve()
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print 'optimal objective value = ', solver.objective_value()
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print 'x1 = ', x1.solution_value(), ', reduced_cost = ', x1.reduced_cost()
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print 'x2 = ', x2.solution_value(), ', reduced_cost = ', x2.reduced_cost()
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print 'x3 = ', x3.solution_value(), ', reduced_cost = ', x3.reduced_cost()
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print 'c0 dual value = ', c0.dual_value()
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print 'c0 activity = ', c0.activity()
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print 'c1 dual value = ', c1.dual_value()
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print 'c1 activity = ', c1.activity()
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print 'c2 dual value = ', c2.dual_value()
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print 'c2 activity = ', c2.activity()
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def RunAllFirstLinearExampleNewAPI(self):
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self.RunFirstLinearExampleNewAPI(pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)
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self.RunFirstLinearExampleNewAPI(pywraplp.Solver.CLP_LINEAR_PROGRAMMING)
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def RunSuccessiveObjectives(self, mode):
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"""Example with succesive objectives."""
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solver = pywraplp.Solver('RunSuccessiveObjectives', mode)
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x1 = solver.NumVar(0, 10, 'var1')
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x2 = solver.NumVar(0, 10, 'var2')
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solver.Add(x1 + 2*x2 <= 10)
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solver.Maximize(x1)
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# Check the solution
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solver.Solve()
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print 'x1 = ', x1.solution_value()
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print 'x2 = ', x2.solution_value()
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solver.Maximize(x2)
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# Check the solution
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solver.Solve()
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print 'x1 = ', x1.solution_value()
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print 'x2 = ', x2.solution_value()
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solver.Minimize(-x1)
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# Check the solution
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solver.Solve()
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print 'x1 = ', x1.solution_value()
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print 'x2 = ', x2.solution_value()
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def RunAllSuccessiveObjectives(self):
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self.RunSuccessiveObjectives(pywraplp.Solver.GLPK_LINEAR_PROGRAMMING)
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self.RunSuccessiveObjectives(pywraplp.Solver.CLP_LINEAR_PROGRAMMING)
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def RunAllExamples(self):
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self.RunAllFirstLinearExample()
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self.RunAllFirstLinearExampleNewAPI()
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self.RunAllSuccessiveObjectives()
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def main(unused_argv):
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lp_example = PyWrapLPExamples()
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lp_example.RunAllExamples()
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if __name__ == '__main__':
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app.run()
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