2025-01-10 11:33:35 +01:00
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// Copyright 2010-2025 Google LLC
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2019-03-25 11:26:14 +01:00
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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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2020-05-26 09:30:42 +02:00
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package com.google.ortools.sat.samples;
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2020-09-11 03:07:18 +02:00
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import com.google.ortools.Loader;
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2019-03-25 11:26:14 +01:00
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import com.google.ortools.sat.CpModel;
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import com.google.ortools.sat.CpSolver;
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import com.google.ortools.sat.CpSolverSolutionCallback;
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2024-05-14 14:43:55 +02:00
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import com.google.ortools.sat.CpSolverStatus;
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2019-03-25 11:26:14 +01:00
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import com.google.ortools.sat.DecisionStrategyProto;
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import com.google.ortools.sat.IntVar;
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import com.google.ortools.sat.Literal;
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import com.google.ortools.sat.SatParameters;
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2019-05-12 18:37:08 +02:00
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import com.google.ortools.util.Domain;
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2019-03-25 11:26:14 +01:00
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/** Link integer constraints together. */
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public class StepFunctionSampleSat {
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public static void main(String[] args) throws Exception {
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2020-09-11 03:07:18 +02:00
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Loader.loadNativeLibraries();
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2019-03-25 11:26:14 +01:00
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// Create the CP-SAT model.
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CpModel model = new CpModel();
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// Declare our primary variable.
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IntVar x = model.newIntVar(0, 20, "x");
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// Create the expression variable and implement the step function
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// Note it is not defined for var == 2.
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//
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// - 3
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// -- -- --------- 2
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// 1
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// -- --- 0
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// 0 ================ 20
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//
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IntVar expr = model.newIntVar(0, 3, "expr");
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// expr == 0 on [5, 6] U [8, 10]
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Literal b0 = model.newBoolVar("b0");
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2019-05-12 18:37:08 +02:00
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model.addLinearExpressionInDomain(x, Domain.fromValues(new long[] {5, 6, 8, 9, 10}))
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.onlyEnforceIf(b0);
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2019-03-25 11:26:14 +01:00
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model.addEquality(expr, 0).onlyEnforceIf(b0);
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// expr == 2 on [0, 1] U [3, 4] U [11, 20]
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Literal b2 = model.newBoolVar("b2");
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2019-05-12 18:37:08 +02:00
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model
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.addLinearExpressionInDomain(
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x, Domain.fromIntervals(new long[][] {{0, 1}, {3, 4}, {11, 20}}))
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.onlyEnforceIf(b2);
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2019-03-25 11:26:14 +01:00
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model.addEquality(expr, 2).onlyEnforceIf(b2);
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// expr == 3 when x = 7
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Literal b3 = model.newBoolVar("b3");
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model.addEquality(x, 7).onlyEnforceIf(b3);
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model.addEquality(expr, 3).onlyEnforceIf(b3);
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// At least one bi is true. (we could use a sum == 1).
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model.addBoolOr(new Literal[] {b0, b2, b3});
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// Search for x values in increasing order.
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model.addDecisionStrategy(new IntVar[] {x},
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DecisionStrategyProto.VariableSelectionStrategy.CHOOSE_FIRST,
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DecisionStrategyProto.DomainReductionStrategy.SELECT_MIN_VALUE);
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// Create the solver.
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CpSolver solver = new CpSolver();
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// Force the solver to follow the decision strategy exactly.
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solver.getParameters().setSearchBranching(SatParameters.SearchBranching.FIXED_SEARCH);
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2021-05-03 12:11:39 +02:00
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// Tell the solver to enumerate all solutions.
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solver.getParameters().setEnumerateAllSolutions(true);
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2019-03-25 11:26:14 +01:00
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// Solve the problem with the printer callback.
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2024-05-14 14:43:55 +02:00
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CpSolverStatus unusedStatus = solver.solve(model, new CpSolverSolutionCallback() {
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2019-03-25 11:26:14 +01:00
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public CpSolverSolutionCallback init(IntVar[] variables) {
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variableArray = variables;
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return this;
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}
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@Override
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public void onSolutionCallback() {
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for (IntVar v : variableArray) {
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System.out.printf("%s=%d ", v.getName(), value(v));
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}
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System.out.println();
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}
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private IntVar[] variableArray;
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}.init(new IntVar[] {x, expr}));
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}
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}
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