MATH-874
First steps to upgrade the API of the classes in "o.a.c.m.optimization.general". Please note the introduction of a "Weight" matrix (whereas the current code assumes that the weights are given as an array). git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1402607 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Gilles Sadowski
parent
2796e77e2e
commit
d0d4760c97
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@ -23,10 +23,15 @@ import org.apache.commons.math3.exception.TooManyEvaluationsException;
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import org.apache.commons.math3.exception.DimensionMismatchException;
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import org.apache.commons.math3.exception.NullArgumentException;
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import org.apache.commons.math3.analysis.MultivariateVectorFunction;
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import org.apache.commons.math3.optimization.OptimizationData;
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import org.apache.commons.math3.optimization.InitialGuess;
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import org.apache.commons.math3.optimization.Target;
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import org.apache.commons.math3.optimization.Weight;
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import org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer;
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import org.apache.commons.math3.optimization.ConvergenceChecker;
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import org.apache.commons.math3.optimization.PointVectorValuePair;
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import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
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import org.apache.commons.math3.linear.RealMatrix;
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/**
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* Base class for implementing optimizers for multivariate scalar functions.
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@ -46,12 +51,16 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
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private ConvergenceChecker<PointVectorValuePair> checker;
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/** Target value for the objective functions at optimum. */
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private double[] target;
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/** Weight for the least squares cost computation. */
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/** Weight matrix. */
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private RealMatrix weightMatrix;
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/** Weight for the least squares cost computation.
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* @deprecated
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*/
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private double[] weight;
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/** Initial guess. */
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private double[] start;
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/** Objective function. */
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private MultivariateVectorFunction function;
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private FUNC function;
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/**
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* Simple constructor with default settings.
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@ -101,12 +110,46 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
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return function.value(point);
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}
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/** {@inheritDoc} */
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/** {@inheritDoc}
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*
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* @deprecated As of 3.1. Please use
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* {@link #optimize(int,MultivariateVectorFunction,OptimizationData[])}
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* instead.
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*/
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@Deprecated
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public PointVectorValuePair optimize(int maxEval, FUNC f, double[] t, double[] w,
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double[] startPoint) {
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return optimizeInternal(maxEval, f, t, w, startPoint);
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}
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/**
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* Optimize an objective function.
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*
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* @param maxEval Allowed number of evaluations of the objective function.
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* @param f Objective function.
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* @param optData Optimization data. The following data will be looked for:
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* <ul>
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* <li>{@link Target}</li>
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* <li>{@link Weight}</li>
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* <li>{@link InitialGuess}</li>
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* </ul>
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* @return the point/value pair giving the optimal value of the objective
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* function.
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* @throws TooManyEvaluationsException if the maximal number of
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* evaluations is exceeded.
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* @throws DimensionMismatchException if the initial guess, target, and weight
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* arguments have inconsistent dimensions.
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*
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* @since 3.1
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*/
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protected PointVectorValuePair optimize(int maxEval,
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FUNC f,
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OptimizationData... optData)
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throws TooManyEvaluationsException,
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DimensionMismatchException {
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return optimizeInternal(maxEval, f, optData);
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}
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/**
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* Optimize an objective function.
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* Optimization is considered to be a weighted least-squares minimization.
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@ -126,8 +169,12 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
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* if the maximal number of evaluations is exceeded.
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* @throws org.apache.commons.math3.exception.NullArgumentException if
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* any argument is {@code null}.
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* @deprecated As of 3.1. Please use
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* {@link #optimizeInternal(int,MultivariateVectorFunction,OptimizationData[])}
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* instead.
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*/
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protected PointVectorValuePair optimizeInternal(final int maxEval, final MultivariateVectorFunction f,
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@Deprecated
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protected PointVectorValuePair optimizeInternal(final int maxEval, final FUNC f,
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final double[] t, final double[] w,
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final double[] startPoint) {
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// Checks.
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@ -147,28 +194,122 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
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throw new DimensionMismatchException(t.length, w.length);
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}
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// Reset.
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evaluations.setMaximalCount(maxEval);
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evaluations.resetCount();
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// Store optimization problem characteristics.
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function = f;
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target = t.clone();
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weight = w.clone();
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start = startPoint.clone();
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// Perform computation.
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return doOptimize();
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return optimizeInternal(maxEval, f,
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new Target(t),
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new Weight(w),
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new InitialGuess(startPoint));
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}
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/**
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* Optimize an objective function.
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*
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* @param maxEval Allowed number of evaluations of the objective function.
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* @param f Objective function.
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* @param optData Optimization data. The following data will be looked for:
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* <ul>
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* <li>{@link Target}</li>
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* <li>{@link Weight}</li>
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* <li>{@link InitialGuess}</li>
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* </ul>
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* @return the point/value pair giving the optimal value of the objective
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* function.
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* @throws TooManyEvaluationsException if the maximal number of
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* evaluations is exceeded.
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* @throws DimensionMismatchException if the initial guess, target, and weight
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* arguments have inconsistent dimensions.
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*
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* @since 3.1
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*/
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protected PointVectorValuePair optimizeInternal(int maxEval,
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FUNC f,
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OptimizationData... optData)
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throws TooManyEvaluationsException,
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DimensionMismatchException {
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// Set internal state.
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evaluations.setMaximalCount(maxEval);
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evaluations.resetCount();
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function = f;
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// Retrieve other settings.
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parseOptimizationData(optData);
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// Check input consistency.
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checkParameters();
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// Allow subclasses to reset their own internal state.
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setUp();
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// Perform computation.
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return doOptimize();
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}
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/**
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* Gets the initial values of the optimized parameters.
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*
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* @return the initial guess.
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*/
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public double[] getStartPoint() {
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return start.clone();
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}
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/**
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* Gets the weight matrix of the observations.
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*
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* @return the weight matrix.
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* @since 3.1
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*/
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public RealMatrix getWeight() {
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return weightMatrix.copy();
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}
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/**
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* Gets the observed values to be matched by the objective vector
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* function.
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*
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* @return the target values.
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* @since 3.1
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*/
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public double[] getTarget() {
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return target.clone();
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}
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/**
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* Computes the residuals.
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* The residual is the difference between the observed (target)
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* values and the model (objective function) value, for the given
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* parameters.
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* There is one residual for each element of the vector-valued
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* function.
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*
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* @param point Parameters of the model.
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* @return the residuals.
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* @throws DimensionMismatchException if {@code point} has a wrong
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* length.
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* @since 3.1
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*/
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protected double[] computeResidual(double[] point) {
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if (point.length != start.length) {
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throw new DimensionMismatchException(point.length,
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start.length);
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}
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final double[] objective = computeObjectiveValue(point);
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final double[] residuals = new double[target.length];
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for (int i = 0; i < target.length; i++) {
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residuals[i] = target[i] - objective[i];
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}
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return residuals;
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}
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/**
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* Gets the objective vector function.
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* Note that this access bypasses the evaluation counter.
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*
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* @return the objective vector function.
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* @since 3.1
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*/
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protected FUNC getObjectiveFunction() {
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return function;
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}
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/**
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* Perform the bulk of the optimization algorithm.
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*
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@ -179,14 +320,80 @@ public abstract class BaseAbstractMultivariateVectorOptimizer<FUNC extends Multi
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/**
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* @return a reference to the {@link #target array}.
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* @deprecated As of 3.1.
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*/
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@Deprecated
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protected double[] getTargetRef() {
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return target;
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}
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/**
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* @return a reference to the {@link #weight array}.
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* @deprecated As of 3.1.
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*/
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@Deprecated
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protected double[] getWeightRef() {
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return weight;
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}
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/**
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* Method which a subclass <em>must</em> override whenever its internal
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* state depend on the {@link OptimizationData input} parsed by this base
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* class.
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* It will be called after the parsing step performed in the
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* {@link #optimize(int,MultivariateVectorFunction,OptimizationData[])
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* optimize} method and just before {@link #doOptimize()}.
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*
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* @since 3.1
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*/
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protected void setUp() {
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// XXX Temporary code until the new internal data is used everywhere.
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final int dim = target.length;
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weight = new double[dim];
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for (int i = 0; i < dim; i++) {
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weight[i] = weightMatrix.getEntry(i, i);
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}
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}
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/**
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* Scans the list of (required and optional) optimization data that
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* characterize the problem.
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*
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* @param optData Optimization data. The following data will be looked for:
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* <ul>
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* <li>{@link Target}</li>
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* <li>{@link Weight}</li>
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* <li>{@link InitialGuess}</li>
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* </ul>
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*/
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private void parseOptimizationData(OptimizationData... optData) {
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// The existing values (as set by the previous call) are reused if
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// not provided in the argument list.
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for (OptimizationData data : optData) {
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if (data instanceof Target) {
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target = ((Target) data).getTarget();
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continue;
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}
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if (data instanceof Weight) {
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weightMatrix = ((Weight) data).getWeight();
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continue;
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}
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if (data instanceof InitialGuess) {
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start = ((InitialGuess) data).getInitialGuess();
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continue;
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}
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}
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}
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/**
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* Check parameters consistency.
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*
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* @throws DimensionMismatchException if {@link #target} and
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* {@link #weightMatrix} have inconsistent dimensions.
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*/
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private void checkParameters() {
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if (target.length != weightMatrix.getColumnDimension()) {
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throw new DimensionMismatchException(target.length,
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weightMatrix.getColumnDimension());
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}
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}
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}
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@ -28,6 +28,10 @@ import org.apache.commons.math3.exception.util.LocalizedFormats;
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import org.apache.commons.math3.linear.DecompositionSolver;
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import org.apache.commons.math3.linear.MatrixUtils;
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import org.apache.commons.math3.linear.QRDecomposition;
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import org.apache.commons.math3.optimization.OptimizationData;
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import org.apache.commons.math3.optimization.InitialGuess;
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import org.apache.commons.math3.optimization.Target;
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import org.apache.commons.math3.optimization.Weight;
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import org.apache.commons.math3.optimization.ConvergenceChecker;
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import org.apache.commons.math3.optimization.DifferentiableMultivariateVectorOptimizer;
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import org.apache.commons.math3.optimization.PointVectorValuePair;
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@ -308,8 +312,10 @@ public abstract class AbstractLeastSquaresOptimizer
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}
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/** {@inheritDoc}
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* @deprecated as of 3.1 replaced by {@link #optimize(int,
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* MultivariateDifferentiableVectorFunction, double[], double[], double[])}
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* @deprecated As of 3.1. Please use
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* {@link BaseAbstractMultivariateVectorOptimizer#optimize(int,MultivariateVectorFunction,OptimizationData[])
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* optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)}
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* instead.
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*/
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@Override
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@Deprecated
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@ -317,8 +323,11 @@ public abstract class AbstractLeastSquaresOptimizer
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final DifferentiableMultivariateVectorFunction f,
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final double[] target, final double[] weights,
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final double[] startPoint) {
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return optimize(maxEval, FunctionUtils.toMultivariateDifferentiableVectorFunction(f),
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target, weights, startPoint);
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return optimizeInternal(maxEval,
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FunctionUtils.toMultivariateDifferentiableVectorFunction(f),
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new Target(target),
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new Weight(weights),
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new InitialGuess(startPoint));
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}
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/**
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@ -340,29 +349,78 @@ public abstract class AbstractLeastSquaresOptimizer
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* if the maximal number of evaluations is exceeded.
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* @throws org.apache.commons.math3.exception.NullArgumentException if
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* any argument is {@code null}.
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* @deprecated As of 3.1. Please use
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* {@link BaseAbstractMultivariateVectorOptimizer#optimize(int,MultivariateVectorFunction,OptimizationData[])
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* optimize(int,MultivariateDifferentiableVectorFunction,OptimizationData...)}
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* instead.
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*/
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@Deprecated
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public PointVectorValuePair optimize(final int maxEval,
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final MultivariateDifferentiableVectorFunction f,
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final double[] target, final double[] weights,
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final double[] startPoint) {
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return optimizeInternal(maxEval, f,
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new Target(target),
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new Weight(weights),
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new InitialGuess(startPoint));
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}
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/**
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* Optimize an objective function.
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* Optimization is considered to be a weighted least-squares minimization.
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* The cost function to be minimized is
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* <code>∑weight<sub>i</sub>(objective<sub>i</sub> - target<sub>i</sub>)<sup>2</sup></code>
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*
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* @param maxEval Allowed number of evaluations of the objective function.
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* @param f Objective function.
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* @param optData Optimization data. The following data will be looked for:
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* <ul>
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* <li>{@link Target}</li>
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* <li>{@link Weight}</li>
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* <li>{@link InitialGuess}</li>
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* </ul>
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* @return the point/value pair giving the optimal value of the objective
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* function.
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* @throws TooManyEvaluationsException if the maximal number of
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* evaluations is exceeded.
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* @throws DimensionMismatchException if the target, and weight arguments
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* have inconsistent dimensions.
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* @see BaseAbstractMultivariateVectorOptimizer#optimizeInternal(int,MultivariateVectorFunction,OptimizationData[])
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*
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* @since 3.1
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*/
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protected PointVectorValuePair optimizeInternal(final int maxEval,
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final MultivariateDifferentiableVectorFunction f,
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OptimizationData... optData) {
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// XXX Conversion will be removed when the generic argument of the
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// base class becomes "MultivariateDifferentiableVectorFunction".
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return super.optimizeInternal(maxEval, FunctionUtils.toDifferentiableMultivariateVectorFunction(f), optData);
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}
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/** {@inheritDoc} */
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@Override
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protected void setUp() {
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super.setUp();
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// Reset counter.
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jacobianEvaluations = 0;
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// Store least squares problem characteristics.
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jF = f;
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// XXX The conversion won't be necessary when the generic argument of
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// the base class becomes "MultivariateDifferentiableVectorFunction".
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// XXX "jF" is not strictly necessary anymore but is currently more
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// efficient than converting the value returned from "getObjectiveFunction()"
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// every time it is used.
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jF = FunctionUtils.toMultivariateDifferentiableVectorFunction((DifferentiableMultivariateVectorFunction) getObjectiveFunction());
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// Arrays shared with the other private methods.
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point = startPoint.clone();
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rows = target.length;
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point = getStartPoint();
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rows = getTarget().length;
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cols = point.length;
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weightedResidualJacobian = new double[rows][cols];
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this.weightedResiduals = new double[rows];
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cost = Double.POSITIVE_INFINITY;
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return optimizeInternal(maxEval, f, target, weights, startPoint);
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}
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}
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