/* * 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; } } } }