Package org.apache.commons.math3.optim

Generally, optimizers are algorithms that will either minimize or maximize a scalar function, called the objective function.

See:
          Description

Interface Summary
ConvergenceChecker<PAIR> This interface specifies how to check if an optimization algorithm has converged.
OptimizationData Marker interface.
 

Class Summary
AbstractConvergenceChecker<PAIR> Base class for all convergence checker implementations.
BaseMultiStartMultivariateOptimizer<PAIR> Base class multi-start optimizer for a multivariate function.
BaseMultivariateOptimizer<PAIR> Base class for implementing optimizers for multivariate functions.
BaseOptimizer<PAIR> Base class for implementing optimizers.
InitialGuess Starting point (first guess) of the optimization procedure.
MaxEval Maximum number of evaluations of the function to be optimized.
MaxIter Maximum number of iterations performed by an (iterative) algorithm.
PointValuePair This class holds a point and the value of an objective function at that point.
PointVectorValuePair This class holds a point and the vectorial value of an objective function at that point.
SimpleBounds Simple optimization constraints: lower and upper bounds.
SimplePointChecker<PAIR extends Pair<double[],? extends Object>> Simple implementation of the ConvergenceChecker interface using only point coordinates.
SimpleValueChecker Simple implementation of the ConvergenceChecker interface using only objective function values.
SimpleVectorValueChecker Simple implementation of the ConvergenceChecker interface using only objective function values.
 

Package org.apache.commons.math3.optim Description

Generally, optimizers are algorithms that will either minimize or maximize a scalar function, called the objective function.
For some scalar objective functions the gradient can be computed (analytically or numerically). Algorithms that use this knowledge are defined in the org.apache.commons.math3.optim.nonlinear.scalar.gradient package. The algorithms that do not need this additional information are located in the 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 model function: such a model predicts a set of values which the algorithm tries to match with a set of given target values. Those algorithms are located in the org.apache.commons.math3.optim.nonlinear.vector package.
Algorithms that also require the Jacobian matrix of the model are located in the org.apache.commons.math3.optim.nonlinear.vector.jacobian package.
The 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 convert a (vector) model into a (scalar) objective function.

This package provides common functionality for the optimization algorithms. Abstract classes (BaseOptimizer and BaseMultivariateOptimizer) contain boiler-plate code for storing evaluations and iterations counters and a user-defined 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.



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