commons-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From l..@apache.org
Subject svn commit: r758059 - in /commons/proper/math/trunk/src: java/org/apache/commons/math/optimization/general/ site/xdoc/ test/org/apache/commons/math/optimization/general/
Date Tue, 24 Mar 2009 22:16:22 GMT
Author: luc
Date: Tue Mar 24 22:16:21 2009
New Revision: 758059

URL: http://svn.apache.org/viewvc?rev=758059&view=rev
Log:
added an implementation of a non-linear conjugate gradient optimizer

Added:
    commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java   (with props)
    commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java   (with props)
    commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java   (with props)
    commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java   (with props)
    commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java   (with props)
Modified:
    commons/proper/math/trunk/src/site/xdoc/changes.xml

Added: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java?rev=758059&view=auto
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java (added)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java Tue Mar 24 22:16:21 2009
@@ -0,0 +1,187 @@
+/*
+ * 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.general;
+
+import org.apache.commons.math.FunctionEvaluationException;
+import org.apache.commons.math.MaxIterationsExceededException;
+import org.apache.commons.math.analysis.DifferentiableMultivariateRealFunction;
+import org.apache.commons.math.analysis.MultivariateVectorialFunction;
+import org.apache.commons.math.optimization.GoalType;
+import org.apache.commons.math.optimization.OptimizationException;
+import org.apache.commons.math.optimization.RealConvergenceChecker;
+import org.apache.commons.math.optimization.DifferentiableMultivariateRealOptimizer;
+import org.apache.commons.math.optimization.RealPointValuePair;
+import org.apache.commons.math.optimization.SimpleScalarValueChecker;
+
+/**
+ * Base class for implementing optimizers for multivariate scalar functions.
+ * <p>This base class handles the boilerplate methods associated to thresholds
+ * settings, iterations and evaluations counting.</p>
+ * @version $Revision$ $Date$
+ * @since 2.0
+ */
+public abstract class AbstractScalarDifferentiableOptimizer
+    implements DifferentiableMultivariateRealOptimizer{
+
+    /** Default maximal number of iterations allowed. */
+    public static final int DEFAULT_MAX_ITERATIONS = 100;
+
+    /** Serializable version identifier. */
+    private static final long serialVersionUID = 1357126012308766636L;
+
+    /** Maximal number of iterations allowed. */
+    private int maxIterations;
+
+    /** Number of iterations already performed. */
+    private int iterations;
+
+    /** Number of evaluations already performed. */
+    private int evaluations;
+
+    /** Number of gradient evaluations. */
+    private int gradientEvaluations;
+
+    /** Convergence checker. */
+    protected RealConvergenceChecker checker;
+
+    /** Objective function. */
+    private DifferentiableMultivariateRealFunction f;
+
+    /** Objective function gradient. */
+    private MultivariateVectorialFunction gradient;
+
+    /** Type of optimization. */
+    protected GoalType goalType;
+
+    /** Current point set. */
+    protected double[] point;
+
+    /** Simple constructor with default settings.
+     * <p>The convergence check is set to a {@link SimpleScalarValueChecker}
+     * and the maximal number of evaluation is set to its default value.</p>
+     */
+    protected AbstractScalarDifferentiableOptimizer() {
+        setConvergenceChecker(new SimpleScalarValueChecker());
+        setMaxIterations(DEFAULT_MAX_ITERATIONS);
+    }
+
+    /** {@inheritDoc} */
+    public void setMaxIterations(int maxIterations) {
+        this.maxIterations = maxIterations;
+    }
+
+    /** {@inheritDoc} */
+    public int getMaxIterations() {
+        return maxIterations;
+    }
+
+    /** {@inheritDoc} */
+    public int getIterations() {
+        return iterations;
+    }
+
+    /** {@inheritDoc} */
+    public int getEvaluations() {
+        return evaluations;
+    }
+
+    /** {@inheritDoc} */
+    public int getGradientEvaluations() {
+        return gradientEvaluations;
+    }
+
+    /** {@inheritDoc} */
+    public void setConvergenceChecker(RealConvergenceChecker checker) {
+        this.checker = checker;
+    }
+
+    /** {@inheritDoc} */
+    public RealConvergenceChecker getConvergenceChecker() {
+        return checker;
+    }
+
+    /** Increment the iterations counter by 1.
+     * @exception OptimizationException if the maximal number
+     * of iterations is exceeded
+     */
+    protected void incrementIterationsCounter()
+        throws OptimizationException {
+        if (++iterations > maxIterations) {
+            if (++iterations > maxIterations) {
+                throw new OptimizationException(new MaxIterationsExceededException(maxIterations));
+            }
+        }
+    }
+
+    /** 
+     * Compute the gradient vector.
+     * @param point point at which the gradient must be evaluated
+     * @return gradient at the specified point
+     * @exception FunctionEvaluationException if the function gradient
+     */
+    protected double[] computeObjectiveGradient(final double[] point)
+        throws FunctionEvaluationException {
+        ++gradientEvaluations;
+        return gradient.value(point);
+    }
+
+    /** 
+     * Compute the objective function value.
+     * @param point point at which the objective function must be evaluated
+     * @return objective function value at specified point
+     * @exception FunctionEvaluationException if the function cannot be evaluated
+     * or its dimension doesn't match problem dimension
+     */
+    protected double computeObjectiveValue(final double[] point)
+        throws FunctionEvaluationException {
+        ++evaluations;
+        return f.value(point);
+    }
+
+    /** {@inheritDoc} */
+    public RealPointValuePair optimize(final DifferentiableMultivariateRealFunction f,
+                                         final GoalType goalType,
+                                         final double[] startPoint)
+        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException {
+
+        // reset counters
+        iterations          = 0;
+        evaluations         = 0;
+        gradientEvaluations = 0;
+
+        // store optimization problem characteristics
+        this.f        = f;
+        gradient      = f.gradient();
+        this.goalType = goalType;
+        point         = startPoint.clone();
+
+        return doOptimize();
+
+    }
+
+    /** Perform the bulk of optimization algorithm.
+     * @return the point/value pair giving the optimal value for objective function
+     * @exception FunctionEvaluationException if the objective function throws one during
+     * the search
+     * @exception OptimizationException if the algorithm failed to converge
+     * @exception IllegalArgumentException if the start point dimension is wrong
+     */
+    abstract protected RealPointValuePair doOptimize()
+        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException;
+
+}
\ No newline at end of file

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java
------------------------------------------------------------------------------
    svn:eol-style = native

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/AbstractScalarDifferentiableOptimizer.java
------------------------------------------------------------------------------
    svn:keywords = Author Date Id Revision

Added: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java?rev=758059&view=auto
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java (added)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java Tue Mar 24 22:16:21 2009
@@ -0,0 +1,49 @@
+/*
+ * 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.general;
+
+/**
+ * Available choices of update formulas for the &beta; parameter
+ * in {@link NonLinearConjugateGradientOptimizer}.
+ * <p>
+ * The &beta; parameter is used to compute the successive conjugate
+ * search directions. For non-linear conjugate gradients, there are
+ * two formulas to compute &beta;:
+ * <ul>
+ *   <li>Fletcher-Reeves formula</li>
+ *   <li>Polak-Ribi&egrave;re formula</li>
+ * </ul>
+ * On the one hand, the Fletcher-Reeves formula is guaranteed to converge
+ * if the start point is close enough of the optimum whether the
+ * Polak-Ribi&egrave;re formula may not converge in rare cases. On the
+ * other hand, the Polak-Ribi&egrave;re formula is often faster when it
+ * does converge. Polak-Ribi&egrave;re is often used.
+ * <p>
+ * @see NonLinearConjugateGradientOptimizer
+ * @version $Revision$ $Date$
+ * @since 2.0
+ */
+public enum ConjugateGradientFormula {
+
+    /** Fletcher-Reeves formula. */
+    FLETCHER_REEVES,
+
+    /** Polak-Ribi&egrave;re formula. */
+    POLAK_RIBIERE
+
+}

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java
------------------------------------------------------------------------------
    svn:eol-style = native

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/ConjugateGradientFormula.java
------------------------------------------------------------------------------
    svn:keywords = Author Date Id Revision

Added: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java?rev=758059&view=auto
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java (added)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java Tue Mar 24 22:16:21 2009
@@ -0,0 +1,302 @@
+/*
+ * 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.general;
+
+import org.apache.commons.math.ConvergenceException;
+import org.apache.commons.math.FunctionEvaluationException;
+import org.apache.commons.math.analysis.UnivariateRealFunction;
+import org.apache.commons.math.analysis.solvers.BrentSolver;
+import org.apache.commons.math.analysis.solvers.UnivariateRealSolver;
+import org.apache.commons.math.optimization.GoalType;
+import org.apache.commons.math.optimization.OptimizationException;
+import org.apache.commons.math.optimization.DifferentiableMultivariateRealOptimizer;
+import org.apache.commons.math.optimization.RealPointValuePair;
+import org.apache.commons.math.optimization.SimpleVectorialValueChecker;
+
+/** 
+ * Non-linear conjugate gradient optimizer.
+ * <p>
+ * This class supports both the Fletcher-Reeves and the Polak-Ribi&egrave;re
+ * update formulas for the conjugate search directions. It also supports
+ * optional preconditioning.
+ * </p>
+ *
+ * @version $Revision$ $Date$
+ * @since 2.0
+ *
+ */
+
+public class NonLinearConjugateGradientOptimizer
+    extends AbstractScalarDifferentiableOptimizer
+    implements DifferentiableMultivariateRealOptimizer {
+
+    /** Serializable version identifier. */
+    private static final long serialVersionUID = -6545223926568155458L;
+
+    /** Update formula for the beta parameter. */
+    private final ConjugateGradientFormula updateFormula;
+
+    /** Preconditioner (may be null). */
+    private Preconditioner preconditioner;
+
+    /** solver to use in the line search (may be null). */
+    private UnivariateRealSolver solver;
+
+    /** Initial step used to bracket the optimum in line search. */
+    private double initialStep;
+
+    /** Simple constructor with default settings.
+     * <p>The convergence check is set to a {@link SimpleVectorialValueChecker}
+     * and the maximal number of evaluation is set to
+     * {@link AbstractLeastSquaresOptimizer#DEFAULT_MAX_EVALUATIONS}.
+     * @param updateFormula formula to use for updating the &beta; parameter,
+     * must be one of {@link UpdateFormula#FLETCHER_REEVES} or {@link
+     * UpdateFormula#POLAK_RIBIERE}
+     */
+    public NonLinearConjugateGradientOptimizer(final ConjugateGradientFormula updateFormula) {
+        this.updateFormula = updateFormula;
+        preconditioner     = null;
+        solver             = null;
+        initialStep        = 1.0;
+    }
+
+    /**
+     * Set the preconditioner.
+     * @param preconditioner preconditioner to use for next optimization,
+     * may be null to remove an already registered preconditioner
+     */
+    public void setPreconditioner(final Preconditioner preconditioner) {
+        this.preconditioner = preconditioner;
+    }
+
+    /**
+     * Set the solver to use during line search.
+     * @param solver solver to use during line search, may be null
+     * to remove an already registered solver and fall back to the
+     * default {@link BrentSolver Brent solver}.
+     */
+    public void setLineSearchSolver(final UnivariateRealSolver solver) {
+        this.solver = solver;
+    }
+
+    /**
+     * Set the initial step used to bracket the optimum in line search.
+     * <p>
+     * The initial step is a factor with respect to the search direction,
+     * which itself is roughly related to the gradient of the function
+     * </p>
+     * @param initialStep initial step used to bracket the optimum in line search,
+     * if a non-positive value is used, the initial step is reset to its
+     * default value of 1.0
+     */
+    public void setInitialStep(final double initialStep) {
+        if (initialStep <= 0) {
+            this.initialStep = 1.0;
+        } else {
+            this.initialStep = initialStep;
+        }
+    }
+
+    /** {@inheritDoc} */
+    protected RealPointValuePair doOptimize()
+        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException {
+        try {
+
+            // initialization
+            if (preconditioner == null) {
+                preconditioner = new IdentityPreconditioner();
+            }
+            if (solver == null) {
+                solver = new BrentSolver();
+            }
+            final int n = point.length;
+            double[] r = computeObjectiveGradient(point);
+            if (goalType == GoalType.MINIMIZE) {
+                for (int i = 0; i < n; ++i) {
+                    r[i] = -r[i];
+                }
+            }
+
+            // initial search direction
+            double[] steepestDescent = preconditioner.precondition(point, r);
+            double[] searchDirection = steepestDescent.clone();
+
+            double delta = 0;
+            for (int i = 0; i < n; ++i) {
+                delta += r[i] * searchDirection[i];
+            }
+
+            RealPointValuePair current = null;
+            while (true) {
+
+                final double objective = computeObjectiveValue(point);
+                RealPointValuePair previous = current;
+                current = new RealPointValuePair(point, objective);
+                if (previous != null) {
+                    if (checker.converged(getIterations(), previous, current)) {
+                        // we have found an optimum
+                        return current;
+                    }
+                }
+
+                incrementIterationsCounter();
+
+                double dTd = 0;
+                for (final double di : searchDirection) {
+                    dTd += di * di;
+                }
+
+                // find the optimal step in the search direction
+                final UnivariateRealFunction lsf = new LineSearchFunction(searchDirection);
+                final double step = solver.solve(lsf, 0, findUpperBound(lsf, 0, initialStep));
+
+                // validate new point
+                for (int i = 0; i < point.length; ++i) {
+                    point[i] += step * searchDirection[i];
+                }
+                r = computeObjectiveGradient(point);
+                if (goalType == GoalType.MINIMIZE) {
+                    for (int i = 0; i < n; ++i) {
+                        r[i] = -r[i];
+                    }
+                }
+
+                // compute beta
+                final double deltaOld = delta;
+                final double[] newSteepestDescent = preconditioner.precondition(point, r);
+                delta = 0;
+                for (int i = 0; i < n; ++i) {
+                    delta += r[i] * newSteepestDescent[i];
+                }
+
+                final double beta;
+                if (updateFormula == ConjugateGradientFormula.FLETCHER_REEVES) {
+                    beta = delta / deltaOld;
+                } else {
+                    double deltaMid = 0;
+                    for (int i = 0; i < r.length; ++i) {
+                        deltaMid += r[i] * steepestDescent[i];
+                    }                    
+                    beta = (delta - deltaMid) / deltaOld;
+                }
+                steepestDescent = newSteepestDescent;
+
+                // compute conjugate search direction
+                if ((getIterations() % n == 0) || (beta < 0)) {
+                    // break conjugation: reset search direction
+                    searchDirection = steepestDescent.clone();
+                } else {
+                    // compute new conjugate search direction
+                    for (int i = 0; i < n; ++i) {
+                        searchDirection[i] = steepestDescent[i] + beta * searchDirection[i];
+                    }
+                }
+
+            }
+
+        } catch (ConvergenceException ce) {
+            throw new OptimizationException(ce);
+        }
+    }
+
+    /**
+     * Find the upper bound b ensuring bracketing of a root between a and b
+     * @param f function whose root must be bracketed
+     * @param a lower bound of the interval
+     * @param h initial step to try
+     * @return b such that f(a) and f(b) have opposite signs
+     * @exception FunctionEvaluationException if the function cannot be computed
+     * @exception OptimizationException if no bracket can be found
+     */
+    private double findUpperBound(final UnivariateRealFunction f,
+                                  final double a, final double h)
+        throws FunctionEvaluationException, OptimizationException {
+        final double yA = f.value(a);
+        double yB = yA;
+        for (double step = h; step < Double.MAX_VALUE; step *= Math.max(2, yA / yB)) {
+            final double b = a + step;
+            yB = f.value(b);
+            if (yA * yB <= 0) {
+                return b;
+            }
+        }
+        throw new OptimizationException("unable to bracket optimum in line search");
+    }
+
+    /** Default identity preconditioner. */
+    private static class IdentityPreconditioner implements Preconditioner {
+
+        /** Serializable version identifier. */
+        private static final long serialVersionUID = 1868235977809734023L;
+
+        /** {@inheritDoc} */
+        public double[] precondition(double[] variables, double[] r) {
+            return r.clone();
+        }
+
+    }
+
+    /** Internal class for line search.
+     * <p>
+     * The function represented by this class is the dot product of
+     * the objective function gradient and the search direction. Its
+     * value is zero when the gradient is orthogonal to the search
+     * direction, i.e. when the objective function value is a local
+     * extremum along the search direction.
+     * </p>
+     */
+    private class LineSearchFunction implements UnivariateRealFunction {
+
+        /** Serializable version identifier. */
+        private static final long serialVersionUID = 8184683950487801424L;
+
+        /** Search direction. */
+        private final double[] searchDirection;
+
+        /** Simple constructor.
+         * @param searchDirection search direction
+         */
+        public LineSearchFunction(final double[] searchDirection) {
+            this.searchDirection = searchDirection;
+        }
+
+        /** {@inheritDoc} */
+        public double value(double x) throws FunctionEvaluationException {
+
+            // current point in the search direction
+            final double[] shiftedPoint = point.clone();
+            for (int i = 0; i < shiftedPoint.length; ++i) {
+                shiftedPoint[i] += x * searchDirection[i];
+            }
+
+            // gradient of the objective function
+            final double[] gradient = computeObjectiveGradient(shiftedPoint);
+
+            // dot product with the search direction
+            double dotProduct = 0;
+            for (int i = 0; i < gradient.length; ++i) {
+                dotProduct += gradient[i] * searchDirection[i];
+            }
+
+            return dotProduct;
+
+        }
+
+    }
+
+}

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizer.java
------------------------------------------------------------------------------
    svn:mergeinfo = 

Added: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java?rev=758059&view=auto
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java (added)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java Tue Mar 24 22:16:21 2009
@@ -0,0 +1,54 @@
+/*
+ * 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.general;
+
+import java.io.Serializable;
+
+import org.apache.commons.math.FunctionEvaluationException;
+
+/** 
+ * This interface represents a preconditioner for differentiable scalar
+ * objective function optimizers.
+ * @version $Revision$ $Date$
+ * @since 2.0
+ */
+public interface Preconditioner extends Serializable {
+
+    /** 
+     * Precondition a search direction.
+     * <p>
+     * The returned preconditioned search direction must be computed fast or
+     * the algorithm performances will drop drastically. A classical approach
+     * is to compute only the diagonal elements of the hessian and to divide
+     * the raw search direction by these elements if they are all positive.
+     * If at least one of them is negative, it is safer to return a clone of
+     * the raw search direction as if the hessian was the identity matrix. The
+     * rationale for this simplified choice is that a negative diagonal element
+     * means the current point is far from the optimum and preconditioning will
+     * not be efficient anyway in this case.
+     * </p>
+     * @param point current point at which the search direction was computed
+     * @param r raw search direction (i.e. opposite of the gradient)
+     * @return approximation of H<sup>-1</sup>r where H is the objective function hessian
+     * @exception FunctionEvaluationException if no cost can be computed for the parameters
+     * @exception IllegalArgumentException if point dimension is wrong
+     */
+    double[] precondition(double[] point, double[] r)
+        throws FunctionEvaluationException, IllegalArgumentException;
+
+}

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java
------------------------------------------------------------------------------
    svn:eol-style = native

Propchange: commons/proper/math/trunk/src/java/org/apache/commons/math/optimization/general/Preconditioner.java
------------------------------------------------------------------------------
    svn:keywords = Author Date Id Revision

Modified: commons/proper/math/trunk/src/site/xdoc/changes.xml
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/site/xdoc/changes.xml?rev=758059&r1=758058&r2=758059&view=diff
==============================================================================
--- commons/proper/math/trunk/src/site/xdoc/changes.xml (original)
+++ commons/proper/math/trunk/src/site/xdoc/changes.xml Tue Mar 24 22:16:21 2009
@@ -41,6 +41,7 @@
     <release version="2.0" date="TBD" description="TBD">
       <action dev="luc" type="fix" issue="MATH-177" >
         Redesigned the optimization framework for a simpler yet more powerful API.
+        Added non-linear conjugate gradient optimizer.
       </action>
       <action dev="luc" type="fix" issue="MATH-243" due-to="Christian Semrau">
         Fixed an error in computing gcd and lcm for some extreme values at integer

Added: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java?rev=758059&view=auto
==============================================================================
--- commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java (added)
+++ commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java Tue Mar 24 22:16:21 2009
@@ -0,0 +1,505 @@
+/*
+ * 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.general;
+
+import java.awt.geom.Point2D;
+import java.util.ArrayList;
+
+import junit.framework.Test;
+import junit.framework.TestCase;
+import junit.framework.TestSuite;
+
+import org.apache.commons.math.FunctionEvaluationException;
+import org.apache.commons.math.analysis.DifferentiableMultivariateRealFunction;
+import org.apache.commons.math.analysis.MultivariateRealFunction;
+import org.apache.commons.math.analysis.MultivariateVectorialFunction;
+import org.apache.commons.math.analysis.solvers.BrentSolver;
+import org.apache.commons.math.linear.DenseRealMatrix;
+import org.apache.commons.math.linear.RealMatrix;
+import org.apache.commons.math.optimization.GoalType;
+import org.apache.commons.math.optimization.OptimizationException;
+import org.apache.commons.math.optimization.RealPointValuePair;
+import org.apache.commons.math.optimization.SimpleScalarValueChecker;
+
+/**
+ * <p>Some of the unit tests are re-implementations of the MINPACK <a
+ * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a
+ * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files. 
+ * The redistribution policy for MINPACK is available <a
+ * href="http://www.netlib.org/minpack/disclaimer">here</a>, for
+ * convenience, it is reproduced below.</p>
+
+ * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0">
+ * <tr><td>
+ *    Minpack Copyright Notice (1999) University of Chicago.
+ *    All rights reserved
+ * </td></tr>
+ * <tr><td>
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * <ol>
+ *  <li>Redistributions of source code must retain the above copyright
+ *      notice, this list of conditions and the following disclaimer.</li>
+ * <li>Redistributions in binary form must reproduce the above
+ *     copyright notice, this list of conditions and the following
+ *     disclaimer in the documentation and/or other materials provided
+ *     with the distribution.</li>
+ * <li>The end-user documentation included with the redistribution, if any,
+ *     must include the following acknowledgment:
+ *     <code>This product includes software developed by the University of
+ *           Chicago, as Operator of Argonne National Laboratory.</code>
+ *     Alternately, this acknowledgment may appear in the software itself,
+ *     if and wherever such third-party acknowledgments normally appear.</li>
+ * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+ *     WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+ *     UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+ *     THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+ *     IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+ *     OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+ *     OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+ *     OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+ *     USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+ *     THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+ *     DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+ *     UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+ *     BE CORRECTED.</strong></li>
+ * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+ *     HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ *     ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+ *     INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ *     ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+ *     PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+ *     SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+ *     (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+ *     EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+ *     POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li>
+ * <ol></td></tr>
+ * </table>
+
+ * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests)
+ * @author Burton S. Garbow (original fortran minpack tests)
+ * @author Kenneth E. Hillstrom (original fortran minpack tests)
+ * @author Jorge J. More (original fortran minpack tests)
+ * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation)
+ */
+public class NonLinearConjugateGradientOptimizerTest
+extends TestCase {
+
+    public NonLinearConjugateGradientOptimizerTest(String name) {
+        super(name);
+    }
+
+    public void testTrivial() throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 2 } }, new double[] { 3 });
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0 });
+        assertEquals(1.5, optimum.getPoint()[0], 1.0e-10);
+        assertEquals(0.0, optimum.getValue(), 1.0e-10);
+    }
+
+    public void testColumnsPermutation() throws FunctionEvaluationException, OptimizationException {
+
+        LinearProblem problem =
+            new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } },
+                              new double[] { 4.0, 6.0, 1.0 });
+
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0 });
+        assertEquals(7.0, optimum.getPoint()[0], 1.0e-10);
+        assertEquals(3.0, optimum.getPoint()[1], 1.0e-10);
+        assertEquals(0.0, optimum.getValue(), 1.0e-10);
+
+    }
+
+    public void testNoDependency() throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 2, 0, 0, 0, 0, 0 },
+                { 0, 2, 0, 0, 0, 0 },
+                { 0, 0, 2, 0, 0, 0 },
+                { 0, 0, 0, 2, 0, 0 },
+                { 0, 0, 0, 0, 2, 0 },
+                { 0, 0, 0, 0, 0, 2 }
+        }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 });
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
+        for (int i = 0; i < problem.target.length; ++i) {
+            assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10);
+        }
+    }
+
+    public void testOneSet() throws FunctionEvaluationException, OptimizationException {
+
+        LinearProblem problem = new LinearProblem(new double[][] {
+                {  1,  0, 0 },
+                { -1,  1, 0 },
+                {  0, -1, 1 }
+        }, new double[] { 1, 1, 1});
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
+        assertEquals(1.0, optimum.getPoint()[0], 1.0e-10);
+        assertEquals(2.0, optimum.getPoint()[1], 1.0e-10);
+        assertEquals(3.0, optimum.getPoint()[2], 1.0e-10);
+
+    }
+
+    public void testTwoSets() throws FunctionEvaluationException, OptimizationException {
+        final double epsilon = 1.0e-7;
+        LinearProblem problem = new LinearProblem(new double[][] {
+                {  2,  1,   0,  4,       0, 0 },
+                { -4, -2,   3, -7,       0, 0 },
+                {  4,  1,  -2,  8,       0, 0 },
+                {  0, -3, -12, -1,       0, 0 },
+                {  0,  0,   0,  0, epsilon, 1 },
+                {  0,  0,   0,  0,       1, 1 }
+        }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2});
+
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setPreconditioner(new Preconditioner() {
+            private static final long serialVersionUID = -2935127802358453014L;
+            public double[] precondition(double[] point, double[] r) {
+                double[] d = r.clone();
+                d[0] /=  72.0;
+                d[1] /=  30.0;
+                d[2] /= 314.0;
+                d[3] /= 260.0;
+                d[4] /= 2 * (1 + epsilon * epsilon);
+                d[5] /= 4.0;
+                return d;
+            }
+        });
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-13, 1.0e-13));
+
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 });
+        assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10);
+        assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10);
+        assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10);
+        assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10);
+        assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10);
+        assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10);
+
+    }
+
+    public void testNonInversible() throws FunctionEvaluationException, OptimizationException {
+
+        LinearProblem problem = new LinearProblem(new double[][] {
+                {  1, 2, -3 },
+                {  2, 1,  3 },
+                { -3, 0, -9 }
+        }, new double[] { 1, 1, 1 });
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+                optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 });
+        assertTrue(optimum.getValue() > 0.5);
+    }
+
+    public void testIllConditioned() throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem1 = new LinearProblem(new double[][] {
+                { 10.0, 7.0,  8.0,  7.0 },
+                {  7.0, 5.0,  6.0,  5.0 },
+                {  8.0, 6.0, 10.0,  9.0 },
+                {  7.0, 5.0,  9.0, 10.0 }
+        }, new double[] { 32, 23, 33, 31 });
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-13, 1.0e-13));
+        BrentSolver solver = new BrentSolver();
+        solver.setAbsoluteAccuracy(1.0e-15);
+        solver.setRelativeAccuracy(1.0e-15);
+        optimizer.setLineSearchSolver(solver);
+        RealPointValuePair optimum1 =
+            optimizer.optimize(problem1, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
+        assertEquals(1.0, optimum1.getPoint()[0], 1.0e-5);
+        assertEquals(1.0, optimum1.getPoint()[1], 1.0e-5);
+        assertEquals(1.0, optimum1.getPoint()[2], 1.0e-5);
+        assertEquals(1.0, optimum1.getPoint()[3], 1.0e-5);
+
+        LinearProblem problem2 = new LinearProblem(new double[][] {
+                { 10.00, 7.00, 8.10, 7.20 },
+                {  7.08, 5.04, 6.00, 5.00 },
+                {  8.00, 5.98, 9.89, 9.00 },
+                {  6.99, 4.99, 9.00, 9.98 }
+        }, new double[] { 32, 23, 33, 31 });
+        RealPointValuePair optimum2 =
+            optimizer.optimize(problem2, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 });
+        assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-1);
+        assertEquals(137.0, optimum2.getPoint()[1], 1.0e-1);
+        assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-1);
+        assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-1);
+
+    }
+
+    public void testMoreEstimatedParametersSimple()
+        throws FunctionEvaluationException, OptimizationException {
+
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 3.0, 2.0,  0.0, 0.0 },
+                { 0.0, 1.0, -1.0, 1.0 },
+                { 2.0, 0.0,  1.0, 0.0 }
+        }, new double[] { 7.0, 3.0, 5.0 });
+
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 7, 6, 5, 4 });
+        assertEquals(0, optimum.getValue(), 1.0e-10);
+
+    }
+
+    public void testMoreEstimatedParametersUnsorted()
+        throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                 { 1.0, 1.0,  0.0,  0.0, 0.0,  0.0 },
+                 { 0.0, 0.0,  1.0,  1.0, 1.0,  0.0 },
+                 { 0.0, 0.0,  0.0,  0.0, 1.0, -1.0 },
+                 { 0.0, 0.0, -1.0,  1.0, 0.0,  1.0 },
+                 { 0.0, 0.0,  0.0, -1.0, 1.0,  0.0 }
+        }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 });
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 2, 2, 2, 2, 2, 2 });
+        assertEquals(0, optimum.getValue(), 1.0e-10);
+    }
+
+    public void testRedundantEquations() throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 1.0,  1.0 },
+                { 1.0, -1.0 },
+                { 1.0,  3.0 }
+        }, new double[] { 3.0, 1.0, 5.0 });
+
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 1, 1 });
+        assertEquals(2.0, optimum.getPoint()[0], 1.0e-8);
+        assertEquals(1.0, optimum.getPoint()[1], 1.0e-8);
+
+    }
+
+    public void testInconsistentEquations() throws FunctionEvaluationException, OptimizationException {
+        LinearProblem problem = new LinearProblem(new double[][] {
+                { 1.0,  1.0 },
+                { 1.0, -1.0 },
+                { 1.0,  3.0 }
+        }, new double[] { 3.0, 1.0, 4.0 });
+
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-6, 1.0e-6));
+        RealPointValuePair optimum =
+            optimizer.optimize(problem, GoalType.MINIMIZE, new double[] { 1, 1 });
+        assertTrue(optimum.getValue() > 0.1);
+
+    }
+
+    public void testCircleFitting() throws FunctionEvaluationException, OptimizationException {
+        Circle circle = new Circle();
+        circle.addPoint( 30.0,  68.0);
+        circle.addPoint( 50.0,  -6.0);
+        circle.addPoint(110.0, -20.0);
+        circle.addPoint( 35.0,  15.0);
+        circle.addPoint( 45.0,  97.0);
+        NonLinearConjugateGradientOptimizer optimizer =
+            new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE);
+        optimizer.setMaxIterations(100);
+        optimizer.setConvergenceChecker(new SimpleScalarValueChecker(1.0e-30, 1.0e-30));
+        BrentSolver solver = new BrentSolver();
+        solver.setAbsoluteAccuracy(1.0e-13);
+        solver.setRelativeAccuracy(1.0e-15);
+        optimizer.setLineSearchSolver(solver);
+        RealPointValuePair optimum =
+            optimizer.optimize(circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 });
+        Point2D.Double center = new Point2D.Double(optimum.getPointRef()[0], optimum.getPointRef()[1]);
+        assertEquals(69.960161753, circle.getRadius(center), 1.0e-8);
+        assertEquals(96.075902096, center.x, 1.0e-8);
+        assertEquals(48.135167894, center.y, 1.0e-8);
+    }
+
+    private static class LinearProblem implements DifferentiableMultivariateRealFunction {
+
+        private static final long serialVersionUID = 703247177355019415L;
+        final RealMatrix factors;
+        final double[] target;
+        public LinearProblem(double[][] factors, double[] target) {
+            this.factors = new DenseRealMatrix(factors);
+            this.target  = target;
+        }
+
+        private double[] gradient(double[] point) {
+            double[] r = factors.operate(point);
+            for (int i = 0; i < r.length; ++i) {
+                r[i] -= target[i];
+            }
+            double[] p = factors.transpose().operate(r);
+            for (int i = 0; i < p.length; ++i) {
+                p[i] *= 2;
+            }
+            return p;
+        }
+
+        public double value(double[] variables) throws FunctionEvaluationException {
+            double[] y = factors.operate(variables);
+            double sum = 0;
+            for (int i = 0; i < y.length; ++i) {
+                double ri = y[i] - target[i];
+                sum += ri * ri;
+            }
+            return sum;
+        }
+
+        public MultivariateVectorialFunction gradient() {
+            return new MultivariateVectorialFunction() {
+                private static final long serialVersionUID = 2621997811350805819L;
+                public double[] value(double[] point) {
+                    return gradient(point);
+                }
+            };
+        }
+
+        public MultivariateRealFunction partialDerivative(final int k) {
+            return new MultivariateRealFunction() {
+                private static final long serialVersionUID = -6186178619133562011L;
+                public double value(double[] point) {
+                    return gradient(point)[k];
+                }
+            };
+        }
+
+    }
+
+    private static class Circle implements DifferentiableMultivariateRealFunction {
+
+        private static final long serialVersionUID = -4711170319243817874L;
+
+        private ArrayList<Point2D.Double> points;
+
+        public Circle() {
+            points  = new ArrayList<Point2D.Double>();
+        }
+
+        public void addPoint(double px, double py) {
+            points.add(new Point2D.Double(px, py));
+        }
+
+        public int getN() {
+            return points.size();
+        }
+
+        public double getRadius(Point2D.Double center) {
+            double r = 0;
+            for (Point2D.Double point : points) {
+                r += point.distance(center);
+            }
+            return r / points.size();
+        }
+
+        private double[] gradient(double[] point) {
+
+            // optimal radius
+            Point2D.Double center = new Point2D.Double(point[0], point[1]);
+            double radius = getRadius(center);
+
+            // gradient of the sum of squared residuals
+            double dJdX = 0;
+            double dJdY = 0;
+            for (Point2D.Double pk : points) {
+                double dk = pk.distance(center);
+                dJdX += (center.x - pk.x) * (dk - radius) / dk;
+                dJdY += (center.y - pk.y) * (dk - radius) / dk;
+            }
+            dJdX *= 2;
+            dJdY *= 2;
+
+            return new double[] { dJdX, dJdY };
+
+        }
+
+        public double value(double[] variables)
+                throws IllegalArgumentException, FunctionEvaluationException {
+
+            Point2D.Double center = new Point2D.Double(variables[0], variables[1]);
+            double radius = getRadius(center);
+
+            double sum = 0;
+            for (Point2D.Double point : points) {
+                double di = point.distance(center) - radius;
+                sum += di * di;
+            }
+
+            return sum;
+
+        }
+
+        public MultivariateVectorialFunction gradient() {
+            return new MultivariateVectorialFunction() {
+                private static final long serialVersionUID = 3174909643301201710L;
+                public double[] value(double[] point) {
+                    return gradient(point);
+                }
+            };
+        }
+
+        public MultivariateRealFunction partialDerivative(final int k) {
+            return new MultivariateRealFunction() {
+                private static final long serialVersionUID = 3073956364104833888L;
+                public double value(double[] point) {
+                    return gradient(point)[k];
+                }
+            };
+        }
+
+    }
+
+    public static Test suite() {
+        return new TestSuite(NonLinearConjugateGradientOptimizerTest.class);
+    }
+
+}

Propchange: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java
------------------------------------------------------------------------------
    svn:eol-style = native

Propchange: commons/proper/math/trunk/src/test/org/apache/commons/math/optimization/general/NonLinearConjugateGradientOptimizerTest.java
------------------------------------------------------------------------------
    svn:keywords = Author Date Id Revision



Mime
View raw message