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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.
"""Tests for ortools.linear_solver.pywraplp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
from ortools.linear_solver import linear_solver_pb2
from ortools.linear_solver import pywraplp
class PyWrapLpTest(unittest.TestCase):
def RunLinearExampleNaturalLanguageAPI(self, 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')
self.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(self, 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)
self.SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2])
def RunMixedIntegerExampleCppStyleAPI(self, optimization_problem_type):
"""Example of simple mixed integer program with the C++ style API."""
solver = pywraplp.Solver('RunMixedIntegerExampleCppStyle',
optimization_problem_type)
infinity = solver.infinity()
# x1 and x2 are integer non-negative variables.
x1 = solver.IntVar(0.0, infinity, 'x1')
x2 = solver.IntVar(0.0, infinity, 'x2')
# Maximize x1 + 10 * x2.
objective = solver.Objective()
objective.SetCoefficient(x1, 1)
objective.SetCoefficient(x2, 10)
objective.SetMaximization()
# x1 + 7 * x2 <= 17.5.
c0 = solver.Constraint(-infinity, 17.5, 'c0')
c0.SetCoefficient(x1, 1)
c0.SetCoefficient(x2, 7)
# x1 <= 3.5.
c1 = solver.Constraint(-infinity, 3.5, 'c1')
c1.SetCoefficient(x1, 1)
c1.SetCoefficient(x2, 0)
self.SolveAndPrint(solver, [x1, x2], [c0, c1])
def RunBooleanExampleCppStyleAPI(self, optimization_problem_type):
"""Example of simple boolean program with the C++ style API."""
solver = pywraplp.Solver('RunBooleanExampleCppStyle',
optimization_problem_type)
infinity = solver.infinity()
# x1 and x2 are integer non-negative variables.
x1 = solver.BoolVar('x1')
x2 = solver.BoolVar('x2')
# Minimize 2 * x1 + x2.
objective = solver.Objective()
objective.SetCoefficient(x1, 2)
objective.SetCoefficient(x2, 1)
objective.SetMinimization()
# 1 <= x1 + 2 * x2 <= 3.
c0 = solver.Constraint(1, 3, 'c0')
c0.SetCoefficient(x1, 1)
c0.SetCoefficient(x2, 2)
self.SolveAndPrint(solver, [x1, x2], [c0])
def SolveAndPrint(self, 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 testApi(self):
all_names_and_problem_types = (list(
linear_solver_pb2.MPModelRequest.SolverType.items()))
for name, problem_type in all_names_and_problem_types:
with self.subTest(f'{name}: {problem_type}'):
if not pywraplp.Solver.SupportsProblemType(problem_type):
continue
if name.startswith('GUROBI'):
continue
if name.endswith('LINEAR_PROGRAMMING'):
print(('\n------ Linear programming example with %s ------' %
name))
print('\n*** Natural language API ***')
self.RunLinearExampleNaturalLanguageAPI(problem_type)
print('\n*** C++ style API ***')
self.RunLinearExampleCppStyleAPI(problem_type)
elif name.endswith('MIXED_INTEGER_PROGRAMMING'):
print((
'\n------ Mixed Integer programming example with %s ------'
% name))
print('\n*** C++ style API ***')
self.RunMixedIntegerExampleCppStyleAPI(problem_type)
elif name.endswith('INTEGER_PROGRAMMING'):
print(('\n------ Boolean programming example with %s ------' %
name))
print('\n*** C++ style API ***')
self.RunBooleanExampleCppStyleAPI(problem_type)
else:
print('ERROR: %s unsupported' % name)
def testSetHint(self):
print('testSetHint')
solver = pywraplp.Solver('RunBooleanExampleCppStyle',
pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)
infinity = solver.infinity()
# x1 and x2 are integer non-negative variables.
x1 = solver.BoolVar('x1')
x2 = solver.BoolVar('x2')
# Minimize 2 * x1 + x2.
objective = solver.Objective()
objective.SetCoefficient(x1, 2)
objective.SetCoefficient(x2, 1)
objective.SetMinimization()
# 1 <= x1 + 2 * x2 <= 3.
c0 = solver.Constraint(1, 3, 'c0')
c0.SetCoefficient(x1, 1)
c0.SetCoefficient(x2, 2)
solver.SetHint([x1, x2], [1.0, 0.0])
self.assertEqual(2, len(solver.variables()))
self.assertEqual(1, len(solver.constraints()))
def testBopInfeasible(self):
print('testBopInfeasible')
solver = pywraplp.Solver('test', pywraplp.Solver.BOP_INTEGER_PROGRAMMING)
solver.EnableOutput()
x = solver.IntVar(0, 10, "")
solver.Add(x >= 20)
result_status = solver.Solve()
print(result_status) # outputs: 0
if __name__ == '__main__':
unittest.main()