/*
* 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.
*/
/**
* <h2>All classes and sub-packages of this package are deprecated.</h2>
* <h3>Please use their replacements, to be found under
* <ul>
* <li>{@link org.apache.commons.math3.optim}</li>
* <li>{@link org.apache.commons.math3.fitting}</li>
* </ul>
* </h3>
*
* <p>
* This package provides common interfaces for the optimization algorithms
* provided in sub-packages. The main interfaces defines optimizers and convergence
* checkers. The functions that are optimized by the algorithms provided by this
* package and its sub-packages are a subset of the one defined in the <code>analysis</code>
* package, namely the real and vector valued functions. These functions are called
* objective function here. When the goal is to minimize, the functions are often called
* cost function, this name is not used in this package.
* </p>
*
* <p>
* Optimizers are the algorithms that will either minimize or maximize, the objective function
* by changing its input variables set until an optimal set is found. There are only four
* interfaces defining the common behavior of optimizers, one for each supported type of objective
* function:
* <ul>
* <li>{@link org.apache.commons.math3.optimization.univariate.UnivariateOptimizer
* UnivariateOptimizer} for {@link org.apache.commons.math3.analysis.UnivariateFunction
* univariate real functions}</li>
* <li>{@link org.apache.commons.math3.optimization.MultivariateOptimizer
* MultivariateOptimizer} for {@link org.apache.commons.math3.analysis.MultivariateFunction
* multivariate real functions}</li>
* <li>{@link org.apache.commons.math3.optimization.MultivariateDifferentiableOptimizer
* MultivariateDifferentiableOptimizer} for {@link
* org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction
* multivariate differentiable real functions}</li>
* <li>{@link org.apache.commons.math3.optimization.MultivariateDifferentiableVectorOptimizer
* MultivariateDifferentiableVectorOptimizer} for {@link
* org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
* multivariate differentiable vectorial functions}</li>
* </ul>
* </p>
*
* <p>
* Despite there are only four types of supported optimizers, it is possible to optimize a
* transform a {@link org.apache.commons.math3.analysis.MultivariateVectorFunction
* non-differentiable multivariate vectorial function} by converting it to a {@link
* org.apache.commons.math3.analysis.MultivariateFunction non-differentiable multivariate
* real function} thanks to the {@link
* org.apache.commons.math3.optimization.LeastSquaresConverter LeastSquaresConverter} helper class.
* The transformed function can be optimized using any implementation of the {@link
* org.apache.commons.math3.optimization.MultivariateOptimizer MultivariateOptimizer} interface.
* </p>
*
* <p>
* For each of the four types of supported optimizers, there is a special implementation which
* wraps a classical optimizer in order to add it a multi-start feature. This feature call the
* underlying optimizer several times in sequence with different starting points and returns
* the best optimum found or all optima if desired. This is a classical way to prevent being
* trapped into a local extremum when looking for a global one.
* </p>
*
*/
package org.apache.commons.math3.optimization;