BaseAbstractMultivariateVectorOptimizer.java

  1. /*
  2.  * Licensed to the Apache Software Foundation (ASF) under one or more
  3.  * contributor license agreements.  See the NOTICE file distributed with
  4.  * this work for additional information regarding copyright ownership.
  5.  * The ASF licenses this file to You under the Apache License, Version 2.0
  6.  * (the "License"); you may not use this file except in compliance with
  7.  * the License.  You may obtain a copy of the License at
  8.  *
  9.  *      http://www.apache.org/licenses/LICENSE-2.0
  10.  *
  11.  * Unless required by applicable law or agreed to in writing, software
  12.  * distributed under the License is distributed on an "AS IS" BASIS,
  13.  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  14.  * See the License for the specific language governing permissions and
  15.  * limitations under the License.
  16.  */

  18. package org.apache.commons.math3.optimization.direct;

  20. import org.apache.commons.math3.util.Incrementor;
  21. import org.apache.commons.math3.exception.MaxCountExceededException;
  22. import org.apache.commons.math3.exception.TooManyEvaluationsException;
  23. import org.apache.commons.math3.exception.DimensionMismatchException;
  24. import org.apache.commons.math3.exception.NullArgumentException;
  25. import org.apache.commons.math3.analysis.MultivariateVectorFunction;
  26. import org.apache.commons.math3.optimization.OptimizationData;
  27. import org.apache.commons.math3.optimization.InitialGuess;
  28. import org.apache.commons.math3.optimization.Target;
  29. import org.apache.commons.math3.optimization.Weight;
  30. import org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer;
  31. import org.apache.commons.math3.optimization.ConvergenceChecker;
  32. import org.apache.commons.math3.optimization.PointVectorValuePair;
  33. import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
  34. import org.apache.commons.math3.linear.RealMatrix;

  36. /**
  37.  * Base class for implementing optimizers for multivariate scalar functions.
  38.  * This base class handles the boiler-plate methods associated to thresholds
  39.  * settings, iterations and evaluations counting.
  40.  *
  41.  * @param <FUNC> the type of the objective function to be optimized
  42.  *
  43.  * @deprecated As of 3.1 (to be removed in 4.0).
  44.  * @since 3.0
  45.  */
  46. @Deprecated
  47. public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends MultivariateVectorFunction>
  48.     implements BaseMultivariateVectorOptimizer<FUNC> {
  49.     /** Evaluations counter. */
  50.     protected final Incrementor evaluations = new Incrementor();
  51.     /** Convergence checker. */
  52.     private ConvergenceChecker<PointVectorValuePair> checker;
  53.     /** Target value for the objective functions at optimum. */
  54.     private double[] target;
  55.     /** Weight matrix. */
  56.     private RealMatrix weightMatrix;
  57.     /** Weight for the least squares cost computation.
  58.      * @deprecated
  59.      */
  60.     @Deprecated
  61.     private double[] weight;
  62.     /** Initial guess. */
  63.     private double[] start;
  64.     /** Objective function. */
  65.     private FUNC function;

  67.     /**
  68.      * Simple constructor with default settings.
  69.      * The convergence check is set to a {@link SimpleVectorValueChecker}.
  70.      * @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()}
  71.      */
  72.     @Deprecated
  73.     protected BaseAbstractMultivariateVectorOptimizer() {
  74.         this(new SimpleVectorValueChecker());
  75.     }
  76.     /**
  77.      * @param checker Convergence checker.
  78.      */
  79.     protected BaseAbstractMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
  80.         this.checker = checker;
  81.     }

  83.     /** {@inheritDoc} */
  84.     public int getMaxEvaluations() {
  85.         return evaluations.getMaximalCount();
  86.     }

  88.     /** {@inheritDoc} */
  89.     public int getEvaluations() {
  90.         return evaluations.getCount();
  91.     }

  93.     /** {@inheritDoc} */
  94.     public ConvergenceChecker<PointVectorValuePair> getConvergenceChecker() {
  95.         return checker;
  96.     }

  98.     /**
  99.      * Compute the objective function value.
  100.      *
  101.      * @param point Point at which the objective function must be evaluated.
  102.      * @return the objective function value at the specified point.
  103.      * @throws TooManyEvaluationsException if the maximal number of evaluations is
  104.      * exceeded.
  105.      */
  106.     protected double[] computeObjectiveValue(double[] point) {
  107.         try {
  108.             evaluations.incrementCount();
  109.         } catch (MaxCountExceededException e) {
  110.             throw new TooManyEvaluationsException(e.getMax());
  111.         }
  112.         return function.value(point);
  113.     }

  115.     /** {@inheritDoc}
  116.      *
  117.      * @deprecated As of 3.1. Please use
  118.      * {@link #optimize(int,MultivariateVectorFunction,OptimizationData[])}
  119.      * instead.
  120.      */
  121.     @Deprecated
  122.     public PointVectorValuePair optimize(int maxEval, FUNC f, double[] t, double[] w,
  123.                                          double[] startPoint) {
  124.         return optimizeInternal(maxEval, f, t, w, startPoint);
  125.     }

  127.     /**
  128.      * Optimize an objective function.
  129.      *
  130.      * @param maxEval Allowed number of evaluations of the objective function.
  131.      * @param f Objective function.
  132.      * @param optData Optimization data. The following data will be looked for:
  133.      * <ul>
  134.      *  <li>{@link Target}</li>
  135.      *  <li>{@link Weight}</li>
  136.      *  <li>{@link InitialGuess}</li>
  137.      * </ul>
  138.      * @return the point/value pair giving the optimal value of the objective
  139.      * function.
  140.      * @throws TooManyEvaluationsException if the maximal number of
  141.      * evaluations is exceeded.
  142.      * @throws DimensionMismatchException if the initial guess, target, and weight
  143.      * arguments have inconsistent dimensions.
  144.      *
  145.      * @since 3.1
  146.      */
  147.     protected PointVectorValuePair optimize(int maxEval,
  148.                                             FUNC f,
  149.                                             OptimizationData... optData)
  150.         throws TooManyEvaluationsException,
  151.                DimensionMismatchException {
  152.         return optimizeInternal(maxEval, f, optData);
  153.     }

  155.     /**
  156.      * Optimize an objective function.
  157.      * Optimization is considered to be a weighted least-squares minimization.
  158.      * The cost function to be minimized is
  159.      * <code>&sum;weight<sub>i</sub>(objective<sub>i</sub> - target<sub>i</sub>)<sup>2</sup></code>
  160.      *
  161.      * @param f Objective function.
  162.      * @param t Target value for the objective functions at optimum.
  163.      * @param w Weights for the least squares cost computation.
  164.      * @param startPoint Start point for optimization.
  165.      * @return the point/value pair giving the optimal value for objective
  166.      * function.
  167.      * @param maxEval Maximum number of function evaluations.
  168.      * @throws org.apache.commons.math3.exception.DimensionMismatchException
  169.      * if the start point dimension is wrong.
  170.      * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
  171.      * if the maximal number of evaluations is exceeded.
  172.      * @throws org.apache.commons.math3.exception.NullArgumentException if
  173.      * any argument is {@code null}.
  174.      * @deprecated As of 3.1. Please use
  175.      * {@link #optimizeInternal(int,MultivariateVectorFunction,OptimizationData[])}
  176.      * instead.
  177.      */
  178.     @Deprecated
  179.     protected PointVectorValuePair optimizeInternal(final int maxEval, final FUNC f,
  180.                                                     final double[] t, final double[] w,
  181.                                                     final double[] startPoint) {
  182.         // Checks.
  183.         if (f == null) {
  184.             throw new NullArgumentException();
  185.         }
  186.         if (t == null) {
  187.             throw new NullArgumentException();
  188.         }
  189.         if (w == null) {
  190.             throw new NullArgumentException();
  191.         }
  192.         if (startPoint == null) {
  193.             throw new NullArgumentException();
  194.         }
  195.         if (t.length != w.length) {
  196.             throw new DimensionMismatchException(t.length, w.length);
  197.         }

  199.         return optimizeInternal(maxEval, f,
  200.                                 new Target(t),
  201.                                 new Weight(w),
  202.                                 new InitialGuess(startPoint));
  203.     }

  205.     /**
  206.      * Optimize an objective function.
  207.      *
  208.      * @param maxEval Allowed number of evaluations of the objective function.
  209.      * @param f Objective function.
  210.      * @param optData Optimization data. The following data will be looked for:
  211.      * <ul>
  212.      *  <li>{@link Target}</li>
  213.      *  <li>{@link Weight}</li>
  214.      *  <li>{@link InitialGuess}</li>
  215.      * </ul>
  216.      * @return the point/value pair giving the optimal value of the objective
  217.      * function.
  218.      * @throws TooManyEvaluationsException if the maximal number of
  219.      * evaluations is exceeded.
  220.      * @throws DimensionMismatchException if the initial guess, target, and weight
  221.      * arguments have inconsistent dimensions.
  222.      *
  223.      * @since 3.1
  224.      */
  225.     protected PointVectorValuePair optimizeInternal(int maxEval,
  226.                                                     FUNC f,
  227.                                                     OptimizationData... optData)
  228.         throws TooManyEvaluationsException,
  229.                DimensionMismatchException {
  230.         // Set internal state.
  231.         evaluations.setMaximalCount(maxEval);
  232.         evaluations.resetCount();
  233.         function = f;
  234.         // Retrieve other settings.
  235.         parseOptimizationData(optData);
  236.         // Check input consistency.
  237.         checkParameters();
  238.         // Allow subclasses to reset their own internal state.
  239.         setUp();
  240.         // Perform computation.
  241.         return doOptimize();
  242.     }

  244.     /**
  245.      * Gets the initial values of the optimized parameters.
  246.      *
  247.      * @return the initial guess.
  248.      */
  249.     public double[] getStartPoint() {
  250.         return start.clone();
  251.     }

  253.     /**
  254.      * Gets the weight matrix of the observations.
  255.      *
  256.      * @return the weight matrix.
  257.      * @since 3.1
  258.      */
  259.     public RealMatrix getWeight() {
  260.         return weightMatrix.copy();
  261.     }
  262.     /**
  263.      * Gets the observed values to be matched by the objective vector
  264.      * function.
  265.      *
  266.      * @return the target values.
  267.      * @since 3.1
  268.      */
  269.     public double[] getTarget() {
  270.         return target.clone();
  271.     }

  273.     /**
  274.      * Gets the objective vector function.
  275.      * Note that this access bypasses the evaluation counter.
  276.      *
  277.      * @return the objective vector function.
  278.      * @since 3.1
  279.      */
  280.     protected FUNC getObjectiveFunction() {
  281.         return function;
  282.     }

  284.     /**
  285.      * Perform the bulk of the optimization algorithm.
  286.      *
  287.      * @return the point/value pair giving the optimal value for the
  288.      * objective function.
  289.      */
  290.     protected abstract PointVectorValuePair doOptimize();

  292.     /**
  293.      * @return a reference to the {@link #target array}.
  294.      * @deprecated As of 3.1.
  295.      */
  296.     @Deprecated
  297.     protected double[] getTargetRef() {
  298.         return target;
  299.     }
  300.     /**
  301.      * @return a reference to the {@link #weight array}.
  302.      * @deprecated As of 3.1.
  303.      */
  304.     @Deprecated
  305.     protected double[] getWeightRef() {
  306.         return weight;
  307.     }

  309.     /**
  310.      * Method which a subclass <em>must</em> override whenever its internal
  311.      * state depend on the {@link OptimizationData input} parsed by this base
  312.      * class.
  313.      * It will be called after the parsing step performed in the
  314.      * {@link #optimize(int,MultivariateVectorFunction,OptimizationData[])
  315.      * optimize} method and just before {@link #doOptimize()}.
  316.      *
  317.      * @since 3.1
  318.      */
  319.     protected void setUp() {
  320.         // XXX Temporary code until the new internal data is used everywhere.
  321.         final int dim = target.length;
  322.         weight = new double[dim];
  323.         for (int i = 0; i < dim; i++) {
  324.             weight[i] = weightMatrix.getEntry(i, i);
  325.         }
  326.     }

  328.     /**
  329.      * Scans the list of (required and optional) optimization data that
  330.      * characterize the problem.
  331.      *
  332.      * @param optData Optimization data. The following data will be looked for:
  333.      * <ul>
  334.      *  <li>{@link Target}</li>
  335.      *  <li>{@link Weight}</li>
  336.      *  <li>{@link InitialGuess}</li>
  337.      * </ul>
  338.      */
  339.     private void parseOptimizationData(OptimizationData... optData) {
  340.         // The existing values (as set by the previous call) are reused if
  341.         // not provided in the argument list.
  342.         for (OptimizationData data : optData) {
  343.             if (data instanceof Target) {
  344.                 target = ((Target) data).getTarget();
  345.                 continue;
  346.             }
  347.             if (data instanceof Weight) {
  348.                 weightMatrix = ((Weight) data).getWeight();
  349.                 continue;
  350.             }
  351.             if (data instanceof InitialGuess) {
  352.                 start = ((InitialGuess) data).getInitialGuess();
  353.                 continue;
  354.             }
  355.         }
  356.     }

  358.     /**
  359.      * Check parameters consistency.
  360.      *
  361.      * @throws DimensionMismatchException if {@link #target} and
  362.      * {@link #weightMatrix} have inconsistent dimensions.
  363.      */
  364.     private void checkParameters() {
  365.         if (target.length != weightMatrix.getColumnDimension()) {
  366.             throw new DimensionMismatchException(target.length,
  367.                                                  weightMatrix.getColumnDimension());
  368.         }
  369.     }
  370. }
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