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* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.
*/
package org.apache.commons.math.optimization.linear;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.RealPointValuePair;
import org.apache.commons.math.util.MathUtils;
/**
* Solves a linear problem using the Two-Phase Simplex Method.
* @version $Revision: 812831 $ $Date: 2009-09-09 10:48:03 +0200 (mer. 09 sept. 2009) $
* @since 2.0
*/
public class SimplexSolver extends AbstractLinearOptimizer {
/** Default amount of error to accept in floating point comparisons. */
private static final double DEFAULT_EPSILON = 1.0e-6;
/** Amount of error to accept in floating point comparisons. */
protected final double epsilon;
/**
* Build a simplex solver with default settings.
*/
public SimplexSolver() {
this(DEFAULT_EPSILON);
}
/**
* Build a simplex solver with a specified accepted amount of error
* @param epsilon the amount of error to accept in floating point comparisons
*/
public SimplexSolver(final double epsilon) {
this.epsilon = epsilon;
}
/**
* Returns the column with the most negative coefficient in the objective function row.
* @param tableau simple tableau for the problem
* @return column with the most negative coefficient
*/
private Integer getPivotColumn(SimplexTableau tableau) {
double minValue = 0;
Integer minPos = null;
for (int i = tableau.getNumObjectiveFunctions(); i < tableau.getWidth() - 1; i++) {
if (MathUtils.compareTo(tableau.getEntry(0, i), minValue, epsilon) < 0) {
minValue = tableau.getEntry(0, i);
minPos = i;
}
}
return minPos;
}
/**
* Returns the row with the minimum ratio as given by the minimum ratio test (MRT).
* @param tableau simple tableau for the problem
* @param col the column to test the ratio of. See {@link #getPivotColumn(SimplexTableau)}
* @return row with the minimum ratio
*/
private Integer getPivotRow(SimplexTableau tableau, final int col) {
// create a list of all the rows that tie for the lowest score in the minimum ratio test
List<Integer> minRatioPositions = new ArrayList<Integer>();
double minRatio = Double.MAX_VALUE;
for (int i = tableau.getNumObjectiveFunctions(); i < tableau.getHeight(); i++) {
final double rhs = tableau.getEntry(i, tableau.getWidth() - 1);
final double entry = tableau.getEntry(i, col);
if (MathUtils.compareTo(entry, 0, epsilon) > 0) {
final double ratio = rhs / entry;
if (MathUtils.equals(ratio, minRatio, epsilon)) {
minRatioPositions.add(i);
} else if (ratio < minRatio) {
minRatio = ratio;
minRatioPositions = new ArrayList<Integer>();
minRatioPositions.add(i);
}
}
}
if (minRatioPositions.size() == 0) {
return null;
} else if (minRatioPositions.size() > 1) {
// there's a degeneracy as indicated by a tie in the minimum ratio test
// check if there's an artificial variable that can be forced out of the basis
for (Integer row : minRatioPositions) {
for (int i = 0; i < tableau.getNumArtificialVariables(); i++) {
int column = i + tableau.getArtificialVariableOffset();
if (MathUtils.equals(tableau.getEntry(row, column), 1, epsilon) &&
row.equals(tableau.getBasicRow(column))) {
return row;
}
}
}
}
return minRatioPositions.get(0);
}
/**
* Runs one iteration of the Simplex method on the given model.
* @param tableau simple tableau for the problem
* @throws OptimizationException if the maximal iteration count has been
* exceeded or if the model is found not to have a bounded solution
*/
protected void doIteration(final SimplexTableau tableau)
throws OptimizationException {
incrementIterationsCounter();
Integer pivotCol = getPivotColumn(tableau);
Integer pivotRow = getPivotRow(tableau, pivotCol);
if (pivotRow == null) {
throw new UnboundedSolutionException();
}
// set the pivot element to 1
double pivotVal = tableau.getEntry(pivotRow, pivotCol);
tableau.divideRow(pivotRow, pivotVal);
// set the rest of the pivot column to 0
for (int i = 0; i < tableau.getHeight(); i++) {
if (i != pivotRow) {
double multiplier = tableau.getEntry(i, pivotCol);
tableau.subtractRow(i, pivotRow, multiplier);
}
}
}
/**
* Solves Phase 1 of the Simplex method.
* @param tableau simple tableau for the problem
* @exception OptimizationException if the maximal number of iterations is
* exceeded, or if the problem is found not to have a bounded solution, or
* if there is no feasible solution
*/
protected void solvePhase1(final SimplexTableau tableau) throws OptimizationException {
// make sure we're in Phase 1
if (tableau.getNumArtificialVariables() == 0) {
return;
}
while (!tableau.isOptimal()) {
doIteration(tableau);
}
// if W is not zero then we have no feasible solution
if (!MathUtils.equals(tableau.getEntry(0, tableau.getRhsOffset()), 0, epsilon)) {
throw new NoFeasibleSolutionException();
}
}
/** {@inheritDoc} */
@Override
public RealPointValuePair doOptimize() throws OptimizationException {
final SimplexTableau tableau =
new SimplexTableau(function, linearConstraints, goal, nonNegative, epsilon);
solvePhase1(tableau);
tableau.dropPhase1Objective();
while (!tableau.isOptimal()) {
doIteration(tableau);
}
return tableau.getSolution();
}
}