79 lines
2.8 KiB
Java
79 lines
2.8 KiB
Java
// Copyright 2010-2018 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.samples;
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import com.google.ortools.Loader;
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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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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.LinearExpr;
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import com.google.ortools.sat.SatParameters;
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/** Link integer constraints together. */
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public class ChannelingSampleSat {
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public static void main(String[] args) throws Exception {
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Loader.loadNativeLibraries();
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// Create the CP-SAT model.
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CpModel model = new CpModel();
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// Declare our two primary variables.
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IntVar x = model.newIntVar(0, 10, "x");
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IntVar y = model.newIntVar(0, 10, "y");
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// Declare our intermediate boolean variable.
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IntVar b = model.newBoolVar("b");
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// Implement b == (x >= 5).
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model.addGreaterOrEqual(x, 5).onlyEnforceIf(b);
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model.addLessOrEqual(x, 4).onlyEnforceIf(b.not());
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// Create our two half-reified constraints.
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// First, b implies (y == 10 - x).
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model.addEquality(LinearExpr.sum(new IntVar[] {x, y}), 10).onlyEnforceIf(b);
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// Second, not(b) implies y == 0.
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model.addEquality(y, 0).onlyEnforceIf(b.not());
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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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// Solve the problem with the printer callback.
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solver.searchAllSolutions(model, new CpSolverSolutionCallback() {
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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, y, b}));
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}
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}
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