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With inspiration from other libraries
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.optimization.direct;
import org.apache.commons.math3.util.Incrementor;
import org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.optimization.OptimizationData;
import org.apache.commons.math3.optimization.InitialGuess;
import org.apache.commons.math3.optimization.Target;
import org.apache.commons.math3.optimization.Weight;
import org.apache.commons.math3.optimization.BaseMultivariateVectorOptimizer;
import org.apache.commons.math3.optimization.ConvergenceChecker;
import org.apache.commons.math3.optimization.PointVectorValuePair;
import org.apache.commons.math3.optimization.SimpleVectorValueChecker;
import org.apache.commons.math3.linear.RealMatrix;
/**
* Base class for implementing optimizers for multivariate scalar functions.
* This base class handles the boiler-plate methods associated to thresholds
* settings, iterations and evaluations counting.
*
* @param the type of the objective function to be optimized
*
* @deprecated As of 3.1 (to be removed in 4.0).
* @since 3.0
*/
@Deprecated
public abstract class BaseAbstractMultivariateVectorOptimizer
implements BaseMultivariateVectorOptimizer {
/** Evaluations counter. */
protected final Incrementor evaluations = new Incrementor();
/** Convergence checker. */
private ConvergenceChecker checker;
/** Target value for the objective functions at optimum. */
private double[] target;
/** Weight matrix. */
private RealMatrix weightMatrix;
/** Weight for the least squares cost computation.
* @deprecated
*/
@Deprecated
private double[] weight;
/** Initial guess. */
private double[] start;
/** Objective function. */
private FUNC function;
/**
* Simple constructor with default settings.
* The convergence check is set to a {@link SimpleVectorValueChecker}.
* @deprecated See {@link SimpleVectorValueChecker#SimpleVectorValueChecker()}
*/
@Deprecated
protected BaseAbstractMultivariateVectorOptimizer() {
this(new SimpleVectorValueChecker());
}
/**
* @param checker Convergence checker.
*/
protected BaseAbstractMultivariateVectorOptimizer(ConvergenceChecker checker) {
this.checker = checker;
}
/** {@inheritDoc} */
public int getMaxEvaluations() {
return evaluations.getMaximalCount();
}
/** {@inheritDoc} */
public int getEvaluations() {
return evaluations.getCount();
}
/** {@inheritDoc} */
public ConvergenceChecker getConvergenceChecker() {
return checker;
}
/**
* Compute the objective function value.
*
* @param point Point at which the objective function must be evaluated.
* @return the objective function value at the specified point.
* @throws TooManyEvaluationsException if the maximal number of evaluations is
* exceeded.
*/
protected double[] computeObjectiveValue(double[] point) {
try {
evaluations.incrementCount();
} catch (MaxCountExceededException e) {
throw new TooManyEvaluationsException(e.getMax());
}
return function.value(point);
}
/** {@inheritDoc}
*
* @deprecated As of 3.1. Please use
* {@link #optimize(int,MultivariateVectorFunction,OptimizationData[])}
* instead.
*/
@Deprecated
public PointVectorValuePair optimize(int maxEval, FUNC f, double[] t, double[] w,
double[] startPoint) {
return optimizeInternal(maxEval, f, t, w, startPoint);
}
/**
* Optimize an objective function.
*
* @param maxEval Allowed number of evaluations of the objective function.
* @param f Objective function.
* @param optData Optimization data. The following data will be looked for:
*
* - {@link Target}
* - {@link Weight}
* - {@link InitialGuess}
*
* @return the point/value pair giving the optimal value of the objective
* function.
* @throws TooManyEvaluationsException if the maximal number of
* evaluations is exceeded.
* @throws DimensionMismatchException if the initial guess, target, and weight
* arguments have inconsistent dimensions.
*
* @since 3.1
*/
protected PointVectorValuePair optimize(int maxEval,
FUNC f,
OptimizationData... optData)
throws TooManyEvaluationsException,
DimensionMismatchException {
return optimizeInternal(maxEval, f, optData);
}
/**
* Optimize an objective function.
* Optimization is considered to be a weighted least-squares minimization.
* The cost function to be minimized is
* ∑weighti(objectivei - targeti)2
*
* @param f Objective function.
* @param t Target value for the objective functions at optimum.
* @param w Weights for the least squares cost computation.
* @param startPoint Start point for optimization.
* @return the point/value pair giving the optimal value for objective
* function.
* @param maxEval Maximum number of function evaluations.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
* @throws org.apache.commons.math3.exception.NullArgumentException if
* any argument is {@code null}.
* @deprecated As of 3.1. Please use
* {@link #optimizeInternal(int,MultivariateVectorFunction,OptimizationData[])}
* instead.
*/
@Deprecated
protected PointVectorValuePair optimizeInternal(final int maxEval, final FUNC f,
final double[] t, final double[] w,
final double[] startPoint) {
// Checks.
if (f == null) {
throw new NullArgumentException();
}
if (t == null) {
throw new NullArgumentException();
}
if (w == null) {
throw new NullArgumentException();
}
if (startPoint == null) {
throw new NullArgumentException();
}
if (t.length != w.length) {
throw new DimensionMismatchException(t.length, w.length);
}
return optimizeInternal(maxEval, f,
new Target(t),
new Weight(w),
new InitialGuess(startPoint));
}
/**
* Optimize an objective function.
*
* @param maxEval Allowed number of evaluations of the objective function.
* @param f Objective function.
* @param optData Optimization data. The following data will be looked for:
*
* - {@link Target}
* - {@link Weight}
* - {@link InitialGuess}
*
* @return the point/value pair giving the optimal value of the objective
* function.
* @throws TooManyEvaluationsException if the maximal number of
* evaluations is exceeded.
* @throws DimensionMismatchException if the initial guess, target, and weight
* arguments have inconsistent dimensions.
*
* @since 3.1
*/
protected PointVectorValuePair optimizeInternal(int maxEval,
FUNC f,
OptimizationData... optData)
throws TooManyEvaluationsException,
DimensionMismatchException {
// Set internal state.
evaluations.setMaximalCount(maxEval);
evaluations.resetCount();
function = f;
// Retrieve other settings.
parseOptimizationData(optData);
// Check input consistency.
checkParameters();
// Allow subclasses to reset their own internal state.
setUp();
// Perform computation.
return doOptimize();
}
/**
* Gets the initial values of the optimized parameters.
*
* @return the initial guess.
*/
public double[] getStartPoint() {
return start.clone();
}
/**
* Gets the weight matrix of the observations.
*
* @return the weight matrix.
* @since 3.1
*/
public RealMatrix getWeight() {
return weightMatrix.copy();
}
/**
* Gets the observed values to be matched by the objective vector
* function.
*
* @return the target values.
* @since 3.1
*/
public double[] getTarget() {
return target.clone();
}
/**
* Gets the objective vector function.
* Note that this access bypasses the evaluation counter.
*
* @return the objective vector function.
* @since 3.1
*/
protected FUNC getObjectiveFunction() {
return function;
}
/**
* Perform the bulk of the optimization algorithm.
*
* @return the point/value pair giving the optimal value for the
* objective function.
*/
protected abstract PointVectorValuePair doOptimize();
/**
* @return a reference to the {@link #target array}.
* @deprecated As of 3.1.
*/
@Deprecated
protected double[] getTargetRef() {
return target;
}
/**
* @return a reference to the {@link #weight array}.
* @deprecated As of 3.1.
*/
@Deprecated
protected double[] getWeightRef() {
return weight;
}
/**
* Method which a subclass must override whenever its internal
* state depend on the {@link OptimizationData input} parsed by this base
* class.
* It will be called after the parsing step performed in the
* {@link #optimize(int,MultivariateVectorFunction,OptimizationData[])
* optimize} method and just before {@link #doOptimize()}.
*
* @since 3.1
*/
protected void setUp() {
// XXX Temporary code until the new internal data is used everywhere.
final int dim = target.length;
weight = new double[dim];
for (int i = 0; i < dim; i++) {
weight[i] = weightMatrix.getEntry(i, i);
}
}
/**
* Scans the list of (required and optional) optimization data that
* characterize the problem.
*
* @param optData Optimization data. The following data will be looked for:
*
* - {@link Target}
* - {@link Weight}
* - {@link InitialGuess}
*
*/
private void parseOptimizationData(OptimizationData... optData) {
// The existing values (as set by the previous call) are reused if
// not provided in the argument list.
for (OptimizationData data : optData) {
if (data instanceof Target) {
target = ((Target) data).getTarget();
continue;
}
if (data instanceof Weight) {
weightMatrix = ((Weight) data).getWeight();
continue;
}
if (data instanceof InitialGuess) {
start = ((InitialGuess) data).getInitialGuess();
continue;
}
}
}
/**
* Check parameters consistency.
*
* @throws DimensionMismatchException if {@link #target} and
* {@link #weightMatrix} have inconsistent dimensions.
*/
private void checkParameters() {
if (target.length != weightMatrix.getColumnDimension()) {
throw new DimensionMismatchException(target.length,
weightMatrix.getColumnDimension());
}
}
}