# Package gov.sandia.cognition.learning.algorithm.minimization.line

Provides line (scalar) minimization algorithms.

See: Description

• Interface Summary
Interface Description
LineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Defines the functionality of a line-minimization algorithm, often called a "line search" algorithm.
• Class Summary
Class Description
AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Partial AnytimeAlgorithm implementation of a LineMinimizer.
DirectionalVectorToDifferentiableScalarFunction
Creates a truly differentiable scalar function from a differentiable Vector function, instead of using a forward-differences approximation to the derivative like DirectionalVectorToScalarFunction does.
DirectionalVectorToScalarFunction
Maps a vector function onto a scalar one by using a directional vector and vector offset, and the parameter to the function is a scalar value along the direction from the start-point offset.
InputOutputSlopeTriplet
Stores an InputOutputPair with corresponding slope (gradient) information
LineBracket
Class that defines a bracket for a scalar function.
LineMinimizerBacktracking
Implementation of the backtracking line-minimization algorithm.
LineMinimizerDerivativeBased
This is an implementation of a line-minimization algorithm proposed by Fletcher that makes extensive use of first-order derivative information.
LineMinimizerDerivativeFree
This is an implementation of a LineMinimizer that does not require derivative information.
WolfeConditions
The Wolfe conditions define a set of sufficient conditions for "sufficient decrease" in inexact line search.

## Package gov.sandia.cognition.learning.algorithm.minimization.line Description

Provides line (scalar) minimization algorithms. These algorithms are primarily used as a subroutine in multivariate minimization algorithms.
Since:
2.1
Author:
Kevin R. Dixon