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 * 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
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package org.apache.commons.math3.optim.nonlinear.vector;

import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.BaseMultivariateOptimizer;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.linear.RealMatrix;

Base class for a multivariate vector function optimizer.
Since:
3.1/**
 * Base class for a multivariate vector function optimizer.
 *
 * @since 3.1
 */
@Deprecated
public abstract class MultivariateVectorOptimizer
    extends BaseMultivariateOptimizer<PointVectorValuePair> {
    
Target values for the model function at optimum. /** Target values for the model function at optimum. */
    private double[] target;
    
Weight matrix. /** Weight matrix. */
    private RealMatrix weightMatrix;
    
Model function. /** Model function. */
    private MultivariateVectorFunction model;

    
Params:
checker – Convergence checker./**
     * @param checker Convergence checker.
     */
    protected MultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
        super(checker);
    }

    
Computes the objective function value.
This method must be called by subclasses to enforce the
evaluation counter limit.
Params:
params – Point at which the objective function must be evaluated.
Throws:
TooManyEvaluationsException – if the maximal number of evaluations
(of the model vector function) is exceeded.
Returns:
the objective function value at the specified point./**
     * Computes the objective function value.
     * This method <em>must</em> be called by subclasses to enforce the
     * evaluation counter limit.
     *
     * @param params 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
     * (of the model vector function) is exceeded.
     */
    protected double[] computeObjectiveValue(double[] params) {
        super.incrementEvaluationCount();
        return model.value(params);
    }

    
{@inheritDoc}
Params:
optData – Optimization data. In addition to those documented in 
BaseMultivariateOptimizer, this method will register the following data: 

 
Target
 
Weight
 
ModelFunction
Throws:
TooManyEvaluationsException – if the maximal number of
evaluations is exceeded.
DimensionMismatchException – if the initial guess, target, and weight
arguments have inconsistent dimensions.
Returns:
{@inheritDoc}/**
     * {@inheritDoc}
     *
     * @param optData Optimization data. In addition to those documented in
     * {@link BaseMultivariateOptimizer#parseOptimizationData(OptimizationData[])
     * BaseMultivariateOptimizer}, this method will register the following data:
     * <ul>
     *  <li>{@link Target}</li>
     *  <li>{@link Weight}</li>
     *  <li>{@link ModelFunction}</li>
     * </ul>
     * @return {@inheritDoc}
     * @throws TooManyEvaluationsException if the maximal number of
     * evaluations is exceeded.
     * @throws DimensionMismatchException if the initial guess, target, and weight
     * arguments have inconsistent dimensions.
     */
    @Override
    public PointVectorValuePair optimize(OptimizationData... optData)
        throws TooManyEvaluationsException,
               DimensionMismatchException {
        // Set up base class and perform computation.
        return super.optimize(optData);
    }

    
Gets the weight matrix of the observations.
Returns:
the weight matrix./**
     * Gets the weight matrix of the observations.
     *
     * @return the weight matrix.
     */
    public RealMatrix getWeight() {
        return weightMatrix.copy();
    }
    
Gets the observed values to be matched by the objective vector
function.
Returns:
the target values./**
     * Gets the observed values to be matched by the objective vector
     * function.
     *
     * @return the target values.
     */
    public double[] getTarget() {
        return target.clone();
    }

    
Gets the number of observed values.
Returns:
the length of the target vector./**
     * Gets the number of observed values.
     *
     * @return the length of the target vector.
     */
    public int getTargetSize() {
        return target.length;
    }

    
Scans the list of (required and optional) optimization data that
characterize the problem.
Params:
optData – Optimization data. The following data will be looked for:

 
Target
 
Weight
 
ModelFunction
/**
     * Scans the list of (required and optional) optimization data that
     * characterize the problem.
     *
     * @param optData Optimization data. The following data will be looked for:
     * <ul>
     *  <li>{@link Target}</li>
     *  <li>{@link Weight}</li>
     *  <li>{@link ModelFunction}</li>
     * </ul>
     */
    @Override
    protected void parseOptimizationData(OptimizationData... optData) {
        // Allow base class to register its own data.
        super.parseOptimizationData(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 ModelFunction) {
                model = ((ModelFunction) data).getModelFunction();
                continue;
            }
            if (data instanceof Target) {
                target = ((Target) data).getTarget();
                continue;
            }
            if (data instanceof Weight) {
                weightMatrix = ((Weight) data).getWeight();
                continue;
            }
        }

        // Check input consistency.
        checkParameters();
    }

    
Check parameters consistency.
Throws:
DimensionMismatchException – if target and weightMatrix have inconsistent dimensions./**
     * 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());
        }
    }
}