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* 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
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* Unless required by applicable law or agreed to in writing, software
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All classes and sub-packages of this package are deprecated.
Please use their replacements, to be found under
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 analysis
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.
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:
UnivariateOptimizer for
univariate real functions
MultivariateOptimizer for
multivariate real functions
MultivariateDifferentiableOptimizer for
multivariate differentiable real functions
MultivariateDifferentiableVectorOptimizer for
multivariate differentiable vectorial functions
Despite there are only four types of supported optimizers, it is possible to optimize a transform a
non-differentiable multivariate vectorial function by converting it to a non-differentiable multivariate
real function thanks to the LeastSquaresConverter helper class. The transformed function can be optimized using any implementation of the MultivariateOptimizer interface.
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.
/**
* <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;