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Add SimpleMipProgram from g3

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This commit is contained in:
Corentin Le Molgat
2018-11-22 09:32:03 +01:00
parent 55417fa088
commit 7e47d2dae5
9 changed files with 363 additions and 4 deletions
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3
makefiles/Makefile.cpp.mk
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@@ -429,7 +429,8 @@ test_cc_graph_samples: \
.PHONY: test_cc_linear_solver_samples # Build and Run all C++ LP Samples (located in ortools/linear_solver/samples)
test_cc_linear_solver_samples: \
rcc_simple_lp_program
rcc_simple_lp_program \
rcc_simple_mip_program
.PHONY: test_cc_sat_samples # Build and Run all C++ Sat Samples (located in ortools/sat/samples)
test_cc_sat_samples: \

3
makefiles/Makefile.dotnet.mk
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@@ -503,7 +503,8 @@ test_dotnet_graph_samples: \
.PHONY: test_dotnet_linear_solver_samples # Build and Run all .Net LP Samples (located in ortools/linear_solver/samples)
test_dotnet_linear_solver_samples: \
rdotnet_SimpleLpProgram.cs
rdotnet_SimpleLpProgram.cs \
rdotnet_SimpleMipProgram.cs
.PHONY: test_dotnet_sat_samples # Build and Run all .Net SAT Samples (located in ortools/sat/samples)
test_dotnet_sat_samples: \

3
makefiles/Makefile.java.mk
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@@ -355,7 +355,8 @@ test_java_graph_samples: \
.PHONY: test_java_linear_solver_samples # Build and Run all Java LP Samples (located in ortools/linear_solver/samples)
test_java_linear_solver_samples: \
rjava_SimpleLpProgram
rjava_SimpleLpProgram \
rjava_SimpleMipProgram
.PHONY: test_java_sat_samples # Build and Run all Java SAT Samples (located in ortools/sat/samples)
test_java_sat_samples: \

3
makefiles/Makefile.python.mk
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@@ -499,7 +499,8 @@ test_python_graph_samples: \
.PHONY: test_python_linear_solver_samples # Run all Python LP Samples (located in ortools/linear_solver/samples)
test_python_linear_solver_samples: \
rpy_simple_lp_program
rpy_simple_lp_program \
rpy_simple_mip_program
.PHONY: test_python_sat_samples # Run all Python Sat Samples (located in ortools/sat/samples)
test_python_sat_samples: \

75
ortools/linear_solver/samples/SimpleMipProgram.cs Normal file
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@@ -0,0 +1,75 @@
// 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.
// [START program]
using System;
using Google.OrTools.LinearSolver;
public class SimpleMipProgram
{
static void Main()
{
// [START solver]
// Create the linear solver with the CBC backend.
Solver solver = Solver.CreateSolver("SimpleMipProgram", "CBC_MIXED_INTEGER_PROGRAMMING");
// [END solver]
// [START variables]
// x and y are integer non-negative variables.
Variable x = solver.MakeIntVar(0.0, double.PositiveInfinity, "x");
Variable y = solver.MakeIntVar(0.0, double.PositiveInfinity, "y");
Console.WriteLine("Number of variables = " + solver.NumVariables());
// [END variables]
// [START constraints]
// x + 7 * y <= 17.5.
solver.Add(x + 7 * y <= 17.5);
// x <= 3.5.
solver.Add(x <= 3.5);
Console.WriteLine("Number of constraints = " + solver.NumConstraints());
// [END constraints]
// [START objective]
// Maximize x + 10 * y.
solver.Maximize(x + 10 * y);
// [END objective]
// [START solve]
int resultStatus = solver.Solve();
// Check that the problem has an optimal solution.
if (resultStatus != Solver.OPTIMAL)
{
Console.WriteLine("The problem does not have an optimal solution!");
return;
}
// [END solve]
// [START print_solution]
Console.WriteLine("Solution:");
Console.WriteLine("Objective value = " + solver.Objective().Value());
Console.WriteLine("x = " + x.SolutionValue());
Console.WriteLine("y = " + y.SolutionValue());
// [END print_solution]
// [START advanced]
Console.WriteLine("\nAdvanced usage:");
Console.WriteLine("Problem solved in " + solver.WallTime() + " milliseconds");
Console.WriteLine("Problem solved in " + solver.Iterations() + " iterations");
Console.WriteLine("Problem solved in " + solver.Nodes() + " branch-and-bound nodes");
// [END advanced]
}
}
// [END program]

20
ortools/linear_solver/samples/SimpleMipProgram.csproj Normal file
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@@ -0,0 +1,20 @@
<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<LangVersion>7.2</LangVersion>
<TargetFramework>netcoreapp2.1</TargetFramework>
<EnableDefaultItems>false</EnableDefaultItems>
<RestoreSources>../../../packages;$(RestoreSources);https://api.nuget.org/v3/index.json</RestoreSources>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|AnyCPU' ">
<DebugType>full</DebugType>
<Optimize>true</Optimize>
<GenerateTailCalls>true</GenerateTailCalls>
</PropertyGroup>
<ItemGroup>
<Compile Include="SimpleMipProgram.cs" />
<PackageReference Include="Google.OrTools" Version="6.9.*" />
</ItemGroup>
</Project>

95
ortools/linear_solver/samples/SimpleMipProgram.java Normal file
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@@ -0,0 +1,95 @@
// 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.
// Minimal example to call the MIP solver.
// [START program]
import com.google.ortools.linearsolver.MPConstraint;
import com.google.ortools.linearsolver.MPObjective;
import com.google.ortools.linearsolver.MPSolver;
import com.google.ortools.linearsolver.MPVariable;
public class SimpleMipProgram {
static {
System.loadLibrary("jniortools");
}
public static void main(String[] args) throws Exception {
// [START solver]
// Create the linear solver with the CBC backend.
MPSolver solver = new MPSolver("SimpleMipProgram",
MPSolver.OptimizationProblemType.valueOf("CBC_MIXED_INTEGER_PROGRAMMING"));
// [END solver]
// [START variables]
double infinity = MPSolver.infinity();
// x and y are integer non-negative variables.
MPVariable x = solver.makeIntVar(0.0, infinity, "x");
MPVariable y = solver.makeIntVar(0.0, infinity, "y");
System.out.println("Number of variables = " + solver.numVariables());
// [END variables]
// [START constraints]
// x + 7 * y <= 17.5.
MPConstraint c0 = solver.makeConstraint(-infinity, 17.5, "c0");
c0.setCoefficient(x, 1);
c0.setCoefficient(y, 7);
// x <= 3.5.
MPConstraint c1 = solver.makeConstraint(-infinity, 3.5, "c1");
c1.setCoefficient(x, 1);
c1.setCoefficient(y, 0);
System.out.println("Number of constraints = " + solver.numConstraints());
// [END constraints]
// [START objective]
// Maximize x + 10 * y.
MPObjective objective = solver.objective();
objective.setCoefficient(x, 1);
objective.setCoefficient(y, 10);
objective.setMaximization();
// [END objective]
// [START solve]
final MPSolver.ResultStatus resultStatus = solver.solve();
// Check that the problem has an optimal solution.
if (resultStatus != MPSolver.ResultStatus.OPTIMAL) {
System.err.println("The problem does not have an optimal solution!");
return;
}
// Verify that the solution satisfies all constraints (when using solvers
// others than GLOP_LINEAR_PROGRAMMING, this is highly recommended!).
if (!solver.verifySolution(/*tolerance=*/1e-7, /*log_errors=*/true)) {
System.err.println("The solution returned by the solver violated the"
+ " problem constraints by at least 1e-7");
return;
}
// [END solve]
// [START print_solution]
System.out.println("Solution:");
System.out.println("Objective value = " + objective.value());
System.out.println("x = " + x.solutionValue());
System.out.println("y = " + y.solutionValue());
// [END print_solution]
// [START advanced]
System.out.println("\nAdvanced usage:");
System.out.println("Problem solved in " + solver.wallTime() + " milliseconds");
System.out.println("Problem solved in " + solver.iterations() + " iterations");
System.out.println("Problem solved in " + solver.nodes() + " branch-and-bound nodes");
// [END advanced]
}
}
// [END program]

86
ortools/linear_solver/samples/simple_mip_program.cc Normal file
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@@ -0,0 +1,86 @@
// 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.
// Mixed Integer programming example that shows how to use the API.
// [START program]
#include "ortools/linear_solver/linear_solver.h"
namespace operations_research {
void simple_mip_program() {
// [START solver]
// Create the mip solver with the CBC backend.
MPSolver solver("simple_mip_program",
MPSolver::CBC_MIXED_INTEGER_PROGRAMMING);
// [END solver]
// [START variables]
const double infinity = solver.infinity();
// x and y are integer non-negative variables.
MPVariable* const x = solver.MakeIntVar(0.0, infinity, "x");
MPVariable* const y = solver.MakeIntVar(0.0, infinity, "y");
LOG(INFO) << "Number of variables = " << solver.NumVariables();
// [END variables]
// [START constraints]
// x + 7 * y <= 17.5.
MPConstraint* const c0 = solver.MakeRowConstraint(-infinity, 17.5, "c0");
c0->SetCoefficient(x, 1);
c0->SetCoefficient(y, 7);
// x <= 3.5.
MPConstraint* const c1 = solver.MakeRowConstraint(-infinity, 3.5, "c1");
c1->SetCoefficient(x, 1);
c1->SetCoefficient(y, 0);
LOG(INFO) << "Number of constraints = " << solver.NumConstraints();
// [END constraints]
// [START objective]
// Maximize x + 10 * y.
MPObjective* const objective = solver.MutableObjective();
objective->SetCoefficient(x, 1);
objective->SetCoefficient(y, 10);
objective->SetMaximization();
// [END objective]
// [START solve]
const MPSolver::ResultStatus result_status = solver.Solve();
// Check that the problem has an optimal solution.
if (result_status != MPSolver::OPTIMAL) {
LOG(FATAL) << "The problem does not have an optimal solution!";
}
// [END solve]
// [START print_solution]
LOG(INFO) << "Solution:";
LOG(INFO) << "x = " << x->solution_value();
LOG(INFO) << "y = " << y->solution_value();
LOG(INFO) << "Optimal objective value = " << objective->Value();
// [END print_solution]
// [START advanced]
LOG(INFO) << "\nAdvanced usage:";
LOG(INFO) << "Problem solved in " << solver.wall_time() << " milliseconds";
LOG(INFO) << "Problem solved in " << solver.iterations() << " iterations";
LOG(INFO) << "Problem solved in " << solver.nodes()
<< " branch-and-bound nodes";
// [END advanced]
}
} // namespace operations_research
int main(int argc, char** argv) {
operations_research::simple_mip_program();
return EXIT_SUCCESS;
}
// [END program]

79
ortools/linear_solver/samples/simple_mip_program.py Normal file
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@@ -0,0 +1,79 @@
# 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.
"""Integer programming examples that show how to use the APIs."""
# [START program]
from __future__ import print_function
from ortools.linear_solver import pywraplp
def main():
# [START solver]
# Create the mip solver with the CBC backend.
solver = pywraplp.Solver('simple_mip_program',
pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
# [END solver]
# [START variables]
infinity = solver.infinity()
# x and y are integer non-negative variables.
x = solver.IntVar(0.0, infinity, 'x')
y = solver.IntVar(0.0, infinity, 'y')
print(('Number of variables = %d' % solver.NumVariables()))
# [END variables]
# [START constraints]
# [END constraints]
# x + 7 * y <= 17.5.
solver.Add(x + 7 * y <= 17.5)
# x <= 3.5.
solver.Add(x <= 3.5)
print(('Number of constraints = ', solver.NumConstraints()))
# [END constraints]
# [START objective]
# Maximize x + 10 * y.
solver.Maximize(x + 10 * y)
# [END objective]
# [START solve]
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)
# [END solve]
# [START print_solution]
print('Solution:')
print('Objective value = ', solver.Objective().Value())
print('x = ', x.solution_value())
print('y = ', y.solution_value())
# [END print_solution]
# [START advanced]
print('\nAdvanced usage:')
print('Problem solved in %f milliseconds' % solver.wall_time())
print('Problem solved in %d iterations' % solver.iterations())
print('Problem solved in %d branch-and-bound nodes' % solver.nodes())
# [END advanced]
if __name__ == '__main__':
main()
# [END program]