Package  Description 

gov.sandia.cognition.learning.algorithm.minimization 
Provides minimization algorithms.

gov.sandia.cognition.learning.algorithm.minimization.line 
Provides line (scalar) minimization algorithms.

Modifier and Type  Class and Description 

class 
FunctionMinimizerBFGS
Implementation of the BroydenFletcherGoldfarbShanno (BFGS) QuasiNewton
nonlinear minimization algorithm.

class 
FunctionMinimizerConjugateGradient
Conjugate gradient method is a class of algorithms for finding the
unconstrained local minimum of a nonlinear function.

class 
FunctionMinimizerDFP
Implementation of the DavidonFletcherPowell (DFP) formula for a
QuasiNewton minimization update.

class 
FunctionMinimizerDirectionSetPowell
Implementation of the derivativefree unconstrained nonlinear directionset
minimization algorithm called "Powell's Method" by Numerical Recipes.

class 
FunctionMinimizerFletcherReeves
This is an implementation of the FletcherReeves conjugate gradient
minimization procedure.

class 
FunctionMinimizerGradientDescent
This is an implementation of the classic Gradient Descent algorithm, also
known as Steepest Descent, Backpropagation (for neural nets), or Hill
Climbing.

class 
FunctionMinimizerLiuStorey
This is an implementation of the LiuStorey conjugate gradient
minimization procedure.

class 
FunctionMinimizerNelderMead
Implementation of the Downhill Simplex minimization algorithm, also known as
the NelderMead method.

class 
FunctionMinimizerPolakRibiere
This is an implementation of the PolackRibiere conjugate gradient
minimization procedure.

class 
FunctionMinimizerQuasiNewton
This is an abstract implementation of the QuasiNewton minimization method,
sometimes called "VariableMetric methods."
This family of minimization algorithms uses firstorder gradient information
to find a locally minimum to a scalar function.

Modifier and Type  Class and Description 

class 
AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Partial AnytimeAlgorithm implementation of a LineMinimizer.

class 
LineMinimizerBacktracking
Implementation of the backtracking lineminimization algorithm.

class 
LineMinimizerDerivativeBased
This is an implementation of a lineminimization algorithm proposed by
Fletcher that makes extensive use of firstorder derivative information.

class 
LineMinimizerDerivativeFree
This is an implementation of a LineMinimizer that does not require
derivative information.
