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lp_test.py
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lp_test.py
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# Copyright 2010-2018 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Linear programming examples that show how to use the APIs."""
from __future__ import print_function
from ortools.linear_solver import linear_solver_pb2
from ortools.linear_solver import pywraplp
def RunLinearExampleNaturalLanguageAPI(optimization_problem_type):
"""Example of simple linear program with natural language API."""
solver = pywraplp.Solver('RunLinearExampleNaturalLanguageAPI',
optimization_problem_type)
infinity = solver.infinity()
# x1, x2 and x3 are continuous non-negative variables.
x1 = solver.NumVar(0.0, infinity, 'x1')
x2 = solver.NumVar(0.0, infinity, 'x2')
x3 = solver.NumVar(0.0, infinity, 'x3')
solver.Maximize(10 * x1 + 6 * x2 + 4 * x3)
c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, 'ConstraintName0')
c1 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)
sum_of_vars = sum([x1, x2, x3])
c2 = solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')
SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2])
# Print a linear expression's solution value.
print(('Sum of vars: %s = %s' % (sum_of_vars, sum_of_vars.solution_value())))
def RunLinearExampleCppStyleAPI(optimization_problem_type):
"""Example of simple linear program with the C++ style API."""
solver = pywraplp.Solver('RunLinearExampleCppStyle',
optimization_problem_type)
infinity = solver.infinity()
# x1, x2 and x3 are continuous non-negative variables.
x1 = solver.NumVar(0.0, infinity, 'x1')
x2 = solver.NumVar(0.0, infinity, 'x2')
x3 = solver.NumVar(0.0, infinity, 'x3')
# Maximize 10 * x1 + 6 * x2 + 4 * x3.
objective = solver.Objective()
objective.SetCoefficient(x1, 10)
objective.SetCoefficient(x2, 6)
objective.SetCoefficient(x3, 4)
objective.SetMaximization()
# x1 + x2 + x3 <= 100.
c0 = solver.Constraint(-infinity, 100.0, 'c0')
c0.SetCoefficient(x1, 1)
c0.SetCoefficient(x2, 1)
c0.SetCoefficient(x3, 1)
# 10 * x1 + 4 * x2 + 5 * x3 <= 600.
c1 = solver.Constraint(-infinity, 600.0, 'c1')
c1.SetCoefficient(x1, 10)
c1.SetCoefficient(x2, 4)
c1.SetCoefficient(x3, 5)
# 2 * x1 + 2 * x2 + 6 * x3 <= 300.
c2 = solver.Constraint(-infinity, 300.0, 'c2')
c2.SetCoefficient(x1, 2)
c2.SetCoefficient(x2, 2)
c2.SetCoefficient(x3, 6)
SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2])
def SolveAndPrint(solver, variable_list, constraint_list):
"""Solve the problem and print the solution."""
print(('Number of variables = %d' % solver.NumVariables()))
print(('Number of constraints = %d' % solver.NumConstraints()))
result_status = solver.Solve()
# The problem has an optimal solution.
assert result_status == pywraplp.Solver.OPTIMAL
# The solution looks legit (when using solvers others than
# GLOP_LINEAR_PROGRAMMING, verifying the solution is highly recommended!).
assert solver.VerifySolution(1e-7, True)
print(('Problem solved in %f milliseconds' % solver.wall_time()))
# The objective value of the solution.
print(('Optimal objective value = %f' % solver.Objective().Value()))
# The value of each variable in the solution.
for variable in variable_list:
print(('%s = %f' % (variable.name(), variable.solution_value())))
print('Advanced usage:')
print(('Problem solved in %d iterations' % solver.iterations()))
for variable in variable_list:
print(
('%s: reduced cost = %f' % (variable.name(), variable.reduced_cost())))
activities = solver.ComputeConstraintActivities()
for i, constraint in enumerate(constraint_list):
print(('constraint %d: dual value = %f\n'
' activity = %f' % (i, constraint.dual_value(),
activities[constraint.index()])))
def main():
all_names_and_problem_types = (
list(linear_solver_pb2.MPModelRequest.SolverType.items()))
for name, problem_type in all_names_and_problem_types:
# Skip non-LP problem types.
if not name.endswith('LINEAR_PROGRAMMING'):
continue
# Skip problem types that aren't supported by the current binary.
if not pywraplp.Solver.SupportsProblemType(problem_type):
continue
print(('\n------ Linear programming example with %s ------' % name))
print('\n*** Natural language API ***')
RunLinearExampleNaturalLanguageAPI(problem_type)
print('\n*** C++ style API ***')
RunLinearExampleCppStyleAPI(problem_type)
if __name__ == '__main__':
main()