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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.
*/
/**
*
* Generally, optimizers are algorithms that will either
* {@link org.apache.commons.math3.optim.nonlinear.scalar.GoalType#MINIMIZE minimize} or
* {@link org.apache.commons.math3.optim.nonlinear.scalar.GoalType#MAXIMIZE maximize}
* a scalar function, called the
* {@link org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction objective
* function}.
*
* For some scalar objective functions the gradient can be computed (analytically
* or numerically). Algorithms that use this knowledge are defined in the
* {@link org.apache.commons.math3.optim.nonlinear.scalar.gradient} package.
* The algorithms that do not need this additional information are located in
* the {@link org.apache.commons.math3.optim.nonlinear.scalar.noderiv} package.
*
*
*
* Some problems are solved more efficiently by algorithms that, instead of an
* objective function, need access to a
* {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunction
* model function}: such a model predicts a set of values which the
* algorithm tries to match with a set of given
* {@link org.apache.commons.math3.optim.nonlinear.vector.Target target values}.
* Those algorithms are located in the
* {@link org.apache.commons.math3.optim.nonlinear.vector} package.
*
* Algorithms that also require the
* {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian
* Jacobian matrix of the model} are located in the
* {@link org.apache.commons.math3.optim.nonlinear.vector.jacobian} package.
*
* The {@link org.apache.commons.math3.optim.nonlinear.vector.jacobian.AbstractLeastSquaresOptimizer
* non-linear least-squares optimizers} are a specialization of the the latter,
* that minimize the distance (called cost or χ2)
* between model and observations.
*
* For cases where the Jacobian cannot be provided, a utility class will
* {@link org.apache.commons.math3.optim.nonlinear.scalar.LeastSquaresConverter
* convert} a (vector) model into a (scalar) objective function.
*
*
*
* This package provides common functionality for the optimization algorithms.
* Abstract classes ({@link org.apache.commons.math3.optim.BaseOptimizer} and
* {@link org.apache.commons.math3.optim.BaseMultivariateOptimizer}) contain
* boiler-plate code for storing {@link org.apache.commons.math3.optim.MaxEval
* evaluations} and {@link org.apache.commons.math3.optim.MaxIter iterations}
* counters and a user-defined
* {@link org.apache.commons.math3.optim.ConvergenceChecker convergence checker}.
*
*
*
* For each of the optimizer types, there is a special implementation that
* wraps an optimizer instance and provides a "multi-start" feature: it calls
* the underlying optimizer several times with different starting points and
* returns the best optimum found, or all optima if so desired.
* This could be useful to avoid being trapped in a local extremum.
*
*/
package org.apache.commons.math3.optim;