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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
* limitations under the License.
*/
package org.apache.commons.math3.optim.nonlinear.vector;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.DimensionMismatchException;
/**
* Base class for implementing optimizers for multivariate vector
* differentiable functions.
* It contains boiler-plate code for dealing with Jacobian evaluation.
* It assumes that the rows of the Jacobian matrix iterate on the model
* functions while the columns iterate on the parameters; thus, the numbers
* of rows is equal to the dimension of the {@link Target} while the
* number of columns is equal to the dimension of the
* {@link org.apache.commons.math3.optim.InitialGuess InitialGuess}.
*
* @since 3.1
* @deprecated All classes and interfaces in this package are deprecated.
* The optimizers that were provided here were moved to the
* {@link org.apache.commons.math3.fitting.leastsquares} package
* (cf. MATH-1008).
*/
@Deprecated
public abstract class JacobianMultivariateVectorOptimizer
extends MultivariateVectorOptimizer {
/**
* Jacobian of the model function.
*/
private MultivariateMatrixFunction jacobian;
/**
* @param checker Convergence checker.
*/
protected JacobianMultivariateVectorOptimizer(ConvergenceChecker<PointVectorValuePair> checker) {
super(checker);
}
/**
* Computes the Jacobian matrix.
*
* @param params Point at which the Jacobian must be evaluated.
* @return the Jacobian at the specified point.
*/
protected double[][] computeJacobian(final double[] params) {
return jacobian.value(params);
}
/**
* {@inheritDoc}
*
* @param optData Optimization data. In addition to those documented in
* {@link MultivariateVectorOptimizer#optimize(OptimizationData...)}
* MultivariateOptimizer}, this method will register the following data:
* <ul>
* <li>{@link ModelFunctionJacobian}</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);
}
/**
* 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 ModelFunctionJacobian}</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 ModelFunctionJacobian) {
jacobian = ((ModelFunctionJacobian) data).getModelFunctionJacobian();
// If more data must be parsed, this statement _must_ be
// changed to "continue".
break;
}
}
}
}
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