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A Java's Collaborative Filtering library to carry out experiments in research of Collaborative Filtering based Recommender Systems. The library has been designed from researchers to researchers.
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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.math3.optimization.linear;

import java.util.ArrayList;
import java.util.List;

import org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.optimization.PointValuePair;
import org.apache.commons.math3.util.Precision;


/**
 * Solves a linear problem using the Two-Phase Simplex Method.
 *
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @since 2.0
 */
@Deprecated
public class SimplexSolver extends AbstractLinearOptimizer {

    /** Default amount of error to accept for algorithm convergence. */
    private static final double DEFAULT_EPSILON = 1.0e-6;

    /** Default amount of error to accept in floating point comparisons (as ulps). */
    private static final int DEFAULT_ULPS = 10;

    /** Amount of error to accept for algorithm convergence. */
    private final double epsilon;

    /** Amount of error to accept in floating point comparisons (as ulps). */
    private final int maxUlps;

    /**
     * Build a simplex solver with default settings.
     */
    public SimplexSolver() {
        this(DEFAULT_EPSILON, DEFAULT_ULPS);
    }

    /**
     * Build a simplex solver with a specified accepted amount of error
     * @param epsilon the amount of error to accept for algorithm convergence
     * @param maxUlps amount of error to accept in floating point comparisons
     */
    public SimplexSolver(final double epsilon, final int maxUlps) {
        this.epsilon = epsilon;
        this.maxUlps = maxUlps;
    }

    /**
     * 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++) {
            final double entry = tableau.getEntry(0, i);
            // check if the entry is strictly smaller than the current minimum
            // do not use a ulp/epsilon check
            if (entry < minValue) {
                minValue = entry;
                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 minRatioPositions = new ArrayList();
        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 (Precision.compareTo(entry, 0d, maxUlps) > 0) {
                final double ratio = rhs / entry;
                // check if the entry is strictly equal to the current min ratio
                // do not use a ulp/epsilon check
                final int cmp = Double.compare(ratio, minRatio);
                if (cmp == 0) {
                    minRatioPositions.add(i);
                } else if (cmp < 0) {
                    minRatio = ratio;
                    minRatioPositions = new ArrayList();
                    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

            // 1. check if there's an artificial variable that can be forced out of the basis
            if (tableau.getNumArtificialVariables() > 0) {
                for (Integer row : minRatioPositions) {
                    for (int i = 0; i < tableau.getNumArtificialVariables(); i++) {
                        int column = i + tableau.getArtificialVariableOffset();
                        final double entry = tableau.getEntry(row, column);
                        if (Precision.equals(entry, 1d, maxUlps) && row.equals(tableau.getBasicRow(column))) {
                            return row;
                        }
                    }
                }
            }

            // 2. apply Bland's rule to prevent cycling:
            //    take the row for which the corresponding basic variable has the smallest index
            //
            // see http://www.stanford.edu/class/msande310/blandrule.pdf
            // see http://en.wikipedia.org/wiki/Bland%27s_rule (not equivalent to the above paper)
            //
            // Additional heuristic: if we did not get a solution after half of maxIterations
            //                       revert to the simple case of just returning the top-most row
            // This heuristic is based on empirical data gathered while investigating MATH-828.
            if (getIterations() < getMaxIterations() / 2) {
                Integer minRow = null;
                int minIndex = tableau.getWidth();
                final int varStart = tableau.getNumObjectiveFunctions();
                final int varEnd = tableau.getWidth() - 1;
                for (Integer row : minRatioPositions) {
                    for (int i = varStart; i < varEnd && !row.equals(minRow); i++) {
                        final Integer basicRow = tableau.getBasicRow(i);
                        if (basicRow != null && basicRow.equals(row) && i < minIndex) {
                            minIndex = i;
                            minRow = row;
                        }
                    }
                }
                return minRow;
            }
        }
        return minRatioPositions.get(0);
    }

    /**
     * Runs one iteration of the Simplex method on the given model.
     * @param tableau simple tableau for the problem
     * @throws MaxCountExceededException if the maximal iteration count has been exceeded
     * @throws UnboundedSolutionException if the model is found not to have a bounded solution
     */
    protected void doIteration(final SimplexTableau tableau)
        throws MaxCountExceededException, UnboundedSolutionException {

        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) {
                final 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
     * @throws MaxCountExceededException if the maximal iteration count has been exceeded
     * @throws UnboundedSolutionException if the model is found not to have a bounded solution
     * @throws NoFeasibleSolutionException if there is no feasible solution
     */
    protected void solvePhase1(final SimplexTableau tableau)
        throws MaxCountExceededException, UnboundedSolutionException, NoFeasibleSolutionException {

        // 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 (!Precision.equals(tableau.getEntry(0, tableau.getRhsOffset()), 0d, epsilon)) {
            throw new NoFeasibleSolutionException();
        }
    }

    /** {@inheritDoc} */
    @Override
    public PointValuePair doOptimize()
        throws MaxCountExceededException, UnboundedSolutionException, NoFeasibleSolutionException {
        final SimplexTableau tableau =
            new SimplexTableau(getFunction(),
                               getConstraints(),
                               getGoalType(),
                               restrictToNonNegative(),
                               epsilon,
                               maxUlps);

        solvePhase1(tableau);
        tableau.dropPhase1Objective();

        while (!tableau.isOptimal()) {
            doIteration(tableau);
        }
        return tableau.getSolution();
    }

}