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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.
*/
/**
*
*
* <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>{@link org.apache.commons.math3.fitting}
* </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>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>{@link org.apache.commons.math3.optimization.MultivariateOptimizer MultivariateOptimizer}
* for {@link org.apache.commons.math3.analysis.MultivariateFunction multivariate real
* functions}
* <li>{@link org.apache.commons.math3.optimization.MultivariateDifferentiableOptimizer
* MultivariateDifferentiableOptimizer} for {@link
* org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction
* multivariate differentiable real functions}
* <li>{@link org.apache.commons.math3.optimization.MultivariateDifferentiableVectorOptimizer
* MultivariateDifferentiableVectorOptimizer} for {@link
* org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
* multivariate differentiable vectorial functions}
* </ul>
*
* <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>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.
*/
package org.apache.commons.math3.optimization;
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