Package org.apache.commons.math.optimization

This package provides common interfaces for the optimization algorithms provided in sub-packages.

See: Description

Interface Summary

Interface	Description
DifferentiableMultivariateRealOptimizer	This interface represents an optimization algorithm for scalar differentiable objective functions.
DifferentiableMultivariateVectorialOptimizer	This interface represents an optimization algorithm for vectorial differentiable objective functions.
MultivariateRealOptimizer	This interface represents an optimization algorithm for scalar objective functions.
RealConvergenceChecker	This interface specifies how to check if an optimization algorithm has converged.
UnivariateRealOptimizer	Interface for (univariate real) optimization algorithms.
VectorialConvergenceChecker	This interface specifies how to check if a optimization algorithm has converged.

Class Summary

Class	Description
LeastSquaresConverter	This class converts vectorial objective functions to scalar objective functions when the goal is to minimize them.
MultiStartDifferentiableMultivariateRealOptimizer	Special implementation of the DifferentiableMultivariateRealOptimizer interface adding multi-start features to an existing optimizer.
MultiStartDifferentiableMultivariateVectorialOptimizer	Special implementation of the DifferentiableMultivariateVectorialOptimizer interface adding multi-start features to an existing optimizer.
MultiStartMultivariateRealOptimizer	Special implementation of the MultivariateRealOptimizer interface adding multi-start features to an existing optimizer.
MultiStartUnivariateRealOptimizer	Special implementation of the UnivariateRealOptimizer interface adding multi-start features to an existing optimizer.
RealPointValuePair	This class holds a point and the value of an objective function at this point.
SimpleRealPointChecker	Simple implementation of the RealConvergenceChecker interface using only point coordinates.
SimpleScalarValueChecker	Simple implementation of the RealConvergenceChecker interface using only objective function values.
SimpleVectorialPointChecker	Simple implementation of the VectorialConvergenceChecker interface using only point coordinates.
SimpleVectorialValueChecker	Simple implementation of the VectorialConvergenceChecker interface using only objective function values.
VectorialPointValuePair	This class holds a point and the vectorial value of an objective function at this point.

Enum Summary

Enum	Description
GoalType	Goal type for an optimization problem.

Exception Summary

Exception	Description
OptimizationException	Deprecated in 2.2 (to be removed in 3.0).

Package org.apache.commons.math.optimization Description

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:

• UnivariateRealOptimizer for univariate real functions
• MultivariateRealOptimizer for multivariate real functions
• DifferentiableMultivariateRealOptimizer for differentiable multivariate real functions
• DifferentiableMultivariateVectorialOptimizer for differentiable multivariate 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 MultivariateRealOptimizer 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.