move examples/test/*.java to ortools/<component>/java
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Mizux Seiha
371
ortools/sat/java/CpSolverTest.java
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371
ortools/sat/java/CpSolverTest.java
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// Copyright 2010-2022 Google LLC
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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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package com.google.ortools.sat;
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import static com.google.common.truth.Truth.assertThat;
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import static org.junit.jupiter.api.Assertions.assertEquals;
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import static org.junit.jupiter.api.Assertions.assertNotNull;
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import com.google.ortools.Loader;
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import com.google.ortools.sat.CpSolverStatus;
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import com.google.ortools.util.Domain;
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import java.util.function.Consumer;
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import org.junit.jupiter.api.BeforeEach;
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import org.junit.jupiter.api.Test;
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/** Tests the CpSolver java interface. */
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public final class CpSolverTest {
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@BeforeEach
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public void setUp() {
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Loader.loadNativeLibraries();
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}
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static class SolutionCounter extends CpSolverSolutionCallback {
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public SolutionCounter() {}
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@Override
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public void onSolutionCallback() {
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solutionCount++;
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}
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private int solutionCount;
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public int getSolutionCount() {
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return solutionCount;
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}
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}
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static class LogToString {
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public LogToString() {
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logBuilder = new StringBuilder();
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}
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public void newMessage(String message) {
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logBuilder.append(message).append("\n");
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}
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private final StringBuilder logBuilder;
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public String getLog() {
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return logBuilder.toString();
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}
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}
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@Test
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public void testCpSolver_solve() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Creates the variables.
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int numVals = 3;
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final IntVar x = model.newIntVar(0, numVals - 1, "x");
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final IntVar y = model.newIntVar(0, numVals - 1, "y");
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// Creates the constraints.
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model.addDifferent(x, y);
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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final CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.OPTIMAL);
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assertThat(solver.value(x)).isNotEqualTo(solver.value(y));
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final String stats = solver.responseStats();
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assertThat(stats).isNotEmpty();
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}
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@Test
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public void testCpSolver_invalidModel() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Creates the variables.
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int numVals = 3;
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final IntVar x = model.newIntVar(0, -1, "x");
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final IntVar y = model.newIntVar(0, numVals - 1, "y");
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// Creates the constraints.
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model.addDifferent(x, y);
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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final CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.MODEL_INVALID);
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assertEquals("var #0 has no domain(): name: \"x\"", solver.getSolutionInfo());
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}
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@Test
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public void testCpSolver_hinting() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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final IntVar x = model.newIntVar(0, 5, "x");
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final IntVar y = model.newIntVar(0, 6, "y");
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// Creates the constraints.
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model.addEquality(LinearExpr.newBuilder().add(x).add(y), 6);
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// Add hints.
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model.addHint(x, 2);
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model.addHint(y, 4);
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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solver.getParameters().setCpModelPresolve(false);
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final CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.OPTIMAL);
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assertThat(solver.value(x)).isEqualTo(2);
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assertThat(solver.value(y)).isEqualTo(4);
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}
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@Test
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public void testCpSolver_booleanValue() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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final BoolVar x = model.newBoolVar("x");
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final BoolVar y = model.newBoolVar("y");
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model.addBoolOr(new Literal[] {x, y.not()});
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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final CpSolverStatus status = solver.solve(model);
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assertEquals(CpSolverStatus.OPTIMAL, status);
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assertThat(solver.booleanValue(x) || solver.booleanValue(y.not())).isTrue();
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}
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@Test
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public void testCpSolver_searchAllSolutions() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Creates the variables.
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int numVals = 3;
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final IntVar x = model.newIntVar(0, numVals - 1, "x");
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final IntVar y = model.newIntVar(0, numVals - 1, "y");
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model.newIntVar(0, numVals - 1, "z");
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// Creates the constraints.
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model.addDifferent(x, y);
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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final SolutionCounter cb = new SolutionCounter();
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solver.searchAllSolutions(model, cb);
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assertThat(cb.getSolutionCount()).isEqualTo(18);
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assertThat(solver.numBranches()).isGreaterThan(0L);
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}
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@Test
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public void testCpSolver_objectiveValue() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Creates the variables.
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final int numVals = 3;
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final IntVar x = model.newIntVar(0, numVals - 1, "x");
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final IntVar y = model.newIntVar(0, numVals - 1, "y");
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final IntVar z = model.newIntVar(0, numVals - 1, "z");
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// Creates the constraints.
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model.addDifferent(x, y);
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// Maximizes a linear combination of variables.
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model.maximize(LinearExpr.newBuilder().add(x).addTerm(y, 2).addTerm(z, 3));
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.OPTIMAL);
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assertThat(solver.objectiveValue()).isEqualTo(11.0);
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assertThat(solver.value(LinearExpr.newBuilder().addSum(new IntVar[] {x, y, z}).build()))
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.isEqualTo(solver.value(x) + solver.value(y) + solver.value(z));
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}
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@Test
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public void testCpModel_crashPresolve() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Create decision variables
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final IntVar x = model.newIntVar(0, 5, "x");
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final IntVar y = model.newIntVar(0, 5, "y");
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// Create a linear constraint which enforces that only x or y can be greater than 0.
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model.addLinearConstraint(LinearExpr.newBuilder().add(x).add(y), 0, 1);
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// Create the objective variable
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final IntVar obj = model.newIntVar(0, 3, "obj");
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// Cut the domain of the objective variable
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model.addGreaterOrEqual(obj, 2);
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// Set a constraint that makes the problem infeasible
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model.addMaxEquality(obj, new IntVar[] {x, y});
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// Optimize objective
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model.minimize(obj);
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// Create a solver and solve the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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com.google.ortools.sat.CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.INFEASIBLE);
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}
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@Test
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public void testCpSolver_customLog() throws Exception {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Creates the variables.
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final int numVals = 3;
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final IntVar x = model.newIntVar(0, numVals - 1, "x");
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final IntVar y = model.newIntVar(0, numVals - 1, "y");
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// Creates the constraints.
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model.addDifferent(x, y);
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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StringBuilder logBuilder = new StringBuilder();
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Consumer<String> appendToLog = (String message) -> logBuilder.append(message).append('\n');
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solver.setLogCallback(appendToLog);
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solver.getParameters().setLogToStdout(false).setLogSearchProgress(true);
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CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.OPTIMAL);
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String log = logBuilder.toString();
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assertThat(log).isNotEmpty();
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assertThat(log).contains("Parameters");
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assertThat(log).contains("log_to_stdout: false");
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assertThat(log).contains("OPTIMAL");
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}
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@Test
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public void testCpSolver_customLogMultiThread() {
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final CpModel model = new CpModel();
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assertNotNull(model);
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// Creates the variables.
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int numVals = 3;
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IntVar x = model.newIntVar(0, numVals - 1, "x");
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IntVar y = model.newIntVar(0, numVals - 1, "y");
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// Creates the constraints.
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model.addDifferent(x, y);
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// Creates a solver and solves the model.
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final CpSolver solver = new CpSolver();
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assertNotNull(solver);
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StringBuilder logBuilder = new StringBuilder();
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Consumer<String> appendToLog = (String message) -> logBuilder.append(message).append('\n');
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solver.setLogCallback(appendToLog);
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solver.getParameters().setLogToStdout(false).setLogSearchProgress(true).setNumSearchWorkers(12);
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CpSolverStatus status = solver.solve(model);
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assertThat(status).isEqualTo(CpSolverStatus.OPTIMAL);
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String log = logBuilder.toString();
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assertThat(log).isNotEmpty();
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assertThat(log).contains("Parameters");
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assertThat(log).contains("log_to_stdout: false");
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assertThat(log).contains("OPTIMAL");
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}
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@Test
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public void issue3108() {
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final CpModel model = new CpModel();
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final IntVar var1 = model.newIntVar(0, 1, "CONTROLLABLE__C1[0]");
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final IntVar var2 = model.newIntVar(0, 1, "CONTROLLABLE__C1[1]");
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capacityConstraint(model, new IntVar[] {var1, var2}, new long[] {0L, 1L},
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new long[][] {new long[] {1L, 1L}}, new long[][] {new long[] {1L, 1L}});
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final CpSolver solver = new CpSolver();
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solver.getParameters().setLogSearchProgress(false);
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solver.getParameters().setCpModelProbingLevel(0);
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solver.getParameters().setNumSearchWorkers(4);
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solver.getParameters().setMaxTimeInSeconds(1);
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final CpSolverStatus status = solver.solve(model);
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assertEquals(status, CpSolverStatus.OPTIMAL);
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}
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private static void capacityConstraint(final CpModel model, final IntVar[] varsToAssign,
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final long[] domainArr, final long[][] demands, final long[][] capacities) {
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final int numTasks = varsToAssign.length;
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final int numResources = demands.length;
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final IntervalVar[] tasksIntervals = new IntervalVar[numTasks + capacities[0].length];
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final Domain domainT = Domain.fromValues(domainArr);
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final Domain intervalRange =
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Domain.fromFlatIntervals(new long[] {domainT.min() + 1, domainT.max() + 1});
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final int unitIntervalSize = 1;
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for (int i = 0; i < numTasks; i++) {
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final BoolVar presence = model.newBoolVar("");
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model.addLinearExpressionInDomain(varsToAssign[i], domainT).onlyEnforceIf(presence);
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model.addLinearExpressionInDomain(varsToAssign[i], domainT.complement())
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.onlyEnforceIf(presence.not());
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// interval with start as taskToNodeAssignment and size of 1
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tasksIntervals[i] =
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model.newOptionalFixedSizeIntervalVar(varsToAssign[i], unitIntervalSize, presence, "");
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}
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// Create dummy intervals
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for (int i = numTasks; i < tasksIntervals.length; i++) {
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final int nodeIndex = i - numTasks;
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tasksIntervals[i] = model.newFixedInterval(domainArr[nodeIndex], 1, "");
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}
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// Convert to list of arrays
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final long[][] nodeCapacities = new long[numResources][];
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final long[] maxCapacities = new long[numResources];
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for (int i = 0; i < capacities.length; i++) {
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final long[] capacityArr = capacities[i];
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long maxCapacityValue = Long.MIN_VALUE;
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for (int j = 0; j < capacityArr.length; j++) {
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maxCapacityValue = Math.max(maxCapacityValue, capacityArr[j]);
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}
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nodeCapacities[i] = capacityArr;
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maxCapacities[i] = maxCapacityValue;
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}
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// For each resource, create dummy demands to accommodate heterogeneous capacities
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final long[][] updatedDemands = new long[numResources][];
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for (int i = 0; i < numResources; i++) {
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final long[] demand = new long[numTasks + capacities[0].length];
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// copy ver task demands
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int iter = 0;
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for (final long taskDemand : demands[i]) {
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demand[iter] = taskDemand;
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iter++;
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}
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// copy over dummy demands
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final long maxCapacity = maxCapacities[i];
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for (final long nodeHeterogeneityAdjustment : nodeCapacities[i]) {
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demand[iter] = maxCapacity - nodeHeterogeneityAdjustment;
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iter++;
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}
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updatedDemands[i] = demand;
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}
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// 2. Capacity constraints
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for (int i = 0; i < numResources; i++) {
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model.addCumulative(maxCapacities[i]).addDemands(tasksIntervals, updatedDemands[i]);
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}
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// Cumulative score
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for (int i = 0; i < numResources; i++) {
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final IntVar max = model.newIntVar(0, maxCapacities[i], "");
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model.addCumulative(max).addDemands(tasksIntervals, updatedDemands[i]).getBuilder();
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model.minimize(max);
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}
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}
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}
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