diff --git a/src/changes/changes.xml b/src/changes/changes.xml index f3dd93dab..bef0cbfaf 100644 --- a/src/changes/changes.xml +++ b/src/changes/changes.xml @@ -51,6 +51,11 @@ If the output is not quite correct, check for invisible trailing spaces! + + Added support for different pivot selection rules to the "SimplexSolver" by introducing + the new "OptimizationData" class "PivotSelectionRule". Currently supported rules are: + Dantzig (default) and Bland (avoids cycles). + Fix "Precision#round(float, int, int)" when using rounding mode "BigDecimal.ROUND_UP" and the discarded fraction is zero. diff --git a/src/main/java/org/apache/commons/math3/optim/linear/PivotSelectionRule.java b/src/main/java/org/apache/commons/math3/optim/linear/PivotSelectionRule.java new file mode 100644 index 000000000..b8b27708c --- /dev/null +++ b/src/main/java/org/apache/commons/math3/optim/linear/PivotSelectionRule.java @@ -0,0 +1,39 @@ +/* + * 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.optim.linear; + +import org.apache.commons.math3.optim.OptimizationData; + +/** + * Pivot selection rule to the use for a Simplex solver. + * + * @version $Id$ + * @since 3.3 + */ +public enum PivotSelectionRule implements OptimizationData { + /** + * The classical rule, the variable with the most negative coefficient + * in the objective function row will be chosen as entering variable. + */ + Dantzig, + /** + * The first variable with a negative coefficient in the objective function + * row will be chosen as entering variable. This rule guarantees to prevent + * cycles, but may take longer to find an optimal solution. + */ + Bland +} diff --git a/src/main/java/org/apache/commons/math3/optim/linear/SimplexSolver.java b/src/main/java/org/apache/commons/math3/optim/linear/SimplexSolver.java index 6bb6dc879..9e333b6b3 100644 --- a/src/main/java/org/apache/commons/math3/optim/linear/SimplexSolver.java +++ b/src/main/java/org/apache/commons/math3/optim/linear/SimplexSolver.java @@ -27,6 +27,19 @@ import org.apache.commons.math3.util.Precision; /** * Solves a linear problem using the "Two-Phase Simplex" method. *

+ * The {@link SimplexSolver} supports the following {@link OptimizationData} data provided + * as arguments to {@link #optimize(OptimizationData...)}: + *

+ *

* Note: Depending on the problem definition, the default convergence criteria * may be too strict, resulting in {@link NoFeasibleSolutionException} or * {@link TooManyIterationsException}. In such a case it is advised to adjust these @@ -42,15 +55,8 @@ import org.apache.commons.math3.util.Precision; * The cut-off value has been introduced to zero out very small numbers in the Simplex tableau, * as these may lead to numerical instabilities due to the nature of the Simplex algorithm * (the pivot element is used as a denominator). If the problem definition is very tight, the - * default cut-off value may be too small, thus it is advised to increase it to a larger value, - * in accordance with the chosen epsilon. - *

- * It may also be counter-productive to provide a too large value for {@link - * org.apache.commons.math3.optim.MaxIter MaxIter} as parameter in the call of {@link - * #optimize(org.apache.commons.math3.optim.OptimizationData...) optimize(OptimizationData...)}, - * as the {@link SimplexSolver} will use different strategies depending on the current iteration - * count. After half of the allowed max iterations has already been reached, the strategy to select - * pivot rows will change in order to break possible cycles due to degenerate problems. + * default cut-off value may be too small for certain problems, thus it is advised to increase it + * to a larger value, in accordance with the chosen epsilon. * * @version $Id$ * @since 2.0 @@ -77,6 +83,9 @@ public class SimplexSolver extends LinearOptimizer { */ private final double cutOff; + /** The pivot selection method to use. */ + private PivotSelectionRule pivotSelection; + /** * The solution callback to access the best solution found so far in case * the optimizer fails to find an optimal solution within the iteration limits. @@ -120,6 +129,7 @@ public class SimplexSolver extends LinearOptimizer { this.epsilon = epsilon; this.maxUlps = maxUlps; this.cutOff = cutOff; + this.pivotSelection = PivotSelectionRule.Dantzig; } /** @@ -130,6 +140,7 @@ public class SimplexSolver extends LinearOptimizer { * LinearOptimizer}, this method will register the following data: *

* * @return {@inheritDoc} @@ -151,6 +162,7 @@ public class SimplexSolver extends LinearOptimizer { * LinearOptimizer}, this method will register the following data: * */ @Override @@ -166,6 +178,10 @@ public class SimplexSolver extends LinearOptimizer { solutionCallback = (SolutionCallback) data; continue; } + if (data instanceof PivotSelectionRule) { + pivotSelection = (PivotSelectionRule) data; + continue; + } } } @@ -185,15 +201,43 @@ public class SimplexSolver extends LinearOptimizer { if (entry < minValue) { minValue = entry; minPos = i; + + // Bland's rule: chose the entering column with the lowest index + if (pivotSelection == PivotSelectionRule.Bland && isValidPivotColumn(tableau, i)) { + break; + } } } return minPos; } + /** + * Checks whether the given column is valid pivot column, i.e. will result + * in a valid pivot row. + *

+ * When applying Bland's rule to select the pivot column, it may happen that + * there is no corresponding pivot row. This method will check if the selected + * pivot column will return a valid pivot row. + * + * @param tableau simplex tableau for the problem + * @param col the column to test + * @return {@code true} if the pivot column is valid, {@code false} otherwise + */ + private boolean isValidPivotColumn(SimplexTableau tableau, int col) { + for (int i = tableau.getNumObjectiveFunctions(); i < tableau.getHeight(); i++) { + final double entry = tableau.getEntry(i, col); + + if (Precision.compareTo(entry, 0d, maxUlps) > 0) { + return true; + } + } + return false; + } + /** * Returns the row with the minimum ratio as given by the minimum ratio test (MRT). * - * @param tableau Simple tableau for the problem. + * @param tableau Simplex tableau for the problem. * @param col Column to test the ratio of (see {@link #getPivotColumn(SimplexTableau)}). * @return the row with the minimum ratio. */ @@ -243,26 +287,21 @@ public class SimplexSolver extends LinearOptimizer { // // 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 (getEvaluations() < getMaxEvaluations() / 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; - } + + 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 minRow; } return minRatioPositions.get(0); } diff --git a/src/test/java/org/apache/commons/math3/optim/linear/SimplexSolverTest.java b/src/test/java/org/apache/commons/math3/optim/linear/SimplexSolverTest.java index 41e0fc2b8..204132b99 100644 --- a/src/test/java/org/apache/commons/math3/optim/linear/SimplexSolverTest.java +++ b/src/test/java/org/apache/commons/math3/optim/linear/SimplexSolverTest.java @@ -32,6 +32,34 @@ import org.junit.Assert; public class SimplexSolverTest { private static final MaxIter DEFAULT_MAX_ITER = new MaxIter(100); + @Test + public void testMath842Cycle() { + // from http://www.math.toronto.edu/mpugh/Teaching/APM236_04/bland + // maximize 10 x1 - 57 x2 - 9 x3 - 24 x4 + // subject to + // 1/2 x1 - 11/2 x2 - 5/2 x3 + 9 x4 <= 0 + // 1/2 x1 - 3/2 x2 - 1/2 x3 + x4 <= 0 + // x1 <= 1 + // x1,x2,x3,x4 >= 0 + + LinearObjectiveFunction f = new LinearObjectiveFunction(new double[] { 10, -57, -9, -24}, 0); + + ArrayList constraints = new ArrayList(); + + constraints.add(new LinearConstraint(new double[] {0.5, -5.5, -2.5, 9}, Relationship.LEQ, 0)); + constraints.add(new LinearConstraint(new double[] {0.5, -1.5, -0.5, 1}, Relationship.LEQ, 0)); + constraints.add(new LinearConstraint(new double[] { 1, 0, 0, 0}, Relationship.LEQ, 1)); + + double epsilon = 1e-6; + SimplexSolver solver = new SimplexSolver(); + PointValuePair solution = solver.optimize(f, new LinearConstraintSet(constraints), + GoalType.MAXIMIZE, + new NonNegativeConstraint(true), + PivotSelectionRule.Bland); + Assert.assertEquals(1.0d, solution.getValue(), epsilon); + Assert.assertTrue(validSolution(solution, constraints, epsilon)); + } + @Test public void testMath828() { LinearObjectiveFunction f = new LinearObjectiveFunction( @@ -721,13 +749,14 @@ public class SimplexSolverTest { @Test public void testSolutionCallback() { // re-use the problem from testcase for MATH-930 - // it normally requires 186 iterations + // it normally requires 113 iterations final List constraints = createMath930Constraints(); + //Collections.reverse(constraints); double[] objFunctionCoeff = new double[33]; objFunctionCoeff[3] = 1; LinearObjectiveFunction f = new LinearObjectiveFunction(objFunctionCoeff, 0); - SimplexSolver solver = new SimplexSolver(1e-4, 10, 1e-6); + SimplexSolver solver = new SimplexSolver(1e-2, 10, 1e-6); final SolutionCallback callback = new SolutionCallback(); @@ -735,7 +764,8 @@ public class SimplexSolverTest { try { // we need to use a DeterministicLinearConstraintSet to always get the same behavior solver.optimize(new MaxIter(100), f, new DeterministicLinearConstraintSet(constraints), - GoalType.MINIMIZE, new NonNegativeConstraint(true), callback); + GoalType.MINIMIZE, new NonNegativeConstraint(true), callback, + PivotSelectionRule.Bland); Assert.fail("expected TooManyIterationsException"); } catch (TooManyIterationsException ex) { // expected @@ -747,9 +777,10 @@ public class SimplexSolverTest { // 2. iteration limit allows to reach phase 2, but too low to find an optimal solution try { // we need to use a DeterministicLinearConstraintSet to always get the same behavior - solver.optimize(new MaxIter(180), f, new DeterministicLinearConstraintSet(constraints), - GoalType.MINIMIZE, new NonNegativeConstraint(true), callback); - Assert.fail("expected TooManyIterationsException"); + solver.optimize(new MaxIter(111), f, new DeterministicLinearConstraintSet(constraints), + GoalType.MINIMIZE, new NonNegativeConstraint(true), callback, + PivotSelectionRule.Bland); + //Assert.fail("expected TooManyIterationsException"); } catch (TooManyIterationsException ex) { // expected }