net.sf.tweety.math.opt

Class GradientDescentRootFinder

java.lang.Object

net.sf.tweety.math.opt.RootFinder

net.sf.tweety.math.opt.OptimizationRootFinder

net.sf.tweety.math.opt.GradientDescentRootFinder

public class GradientDescentRootFinder
extends OptimizationRootFinder

Implements the gradient descent method to find zeros of a (multi-dimensional) function.

Author:

Matthias Thimm

Field Summary

Fields

Modifier and Type	Field and Description
private static org.slf4j.Logger	LOG reference to the logback logger instance
double	precision The precision of the approximation.

Fields inherited from class net.sf.tweety.math.opt.RootFinder

PRECISION

Constructor Summary

Constructors

Constructor and Description
GradientDescentRootFinder(java.util.List<Term> functions, java.util.Map<Variable,Term> startingPoint) Creates a new root finder for the given starting point and the given (multi-dimensional) function
GradientDescentRootFinder(Term function, java.util.Map<Variable,Term> startingPoint) Creates a new root finder for the given starting point and the given function

Method Summary

Modifier and Type	Method and Description
java.util.Map<Variable,Term>	randomRoot() Determines the values for the variables appearing in the function such the function evaluates to zero.

Methods inherited from class net.sf.tweety.math.opt.OptimizationRootFinder

buildOptimizationProblem

Methods inherited from class net.sf.tweety.math.opt.RootFinder

getFunctions, getStartingPoint, setStartingPoint

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

LOG

private static org.slf4j.Logger LOG

reference to the logback logger instance

precision

public double precision

The precision of the approximation. The actual used precision depends on the number of variables.

Constructor Detail

GradientDescentRootFinder

public GradientDescentRootFinder(Term function,
                                 java.util.Map<Variable,Term> startingPoint)

Creates a new root finder for the given starting point and the given function

Parameters:

startingPoint -

GradientDescentRootFinder

public GradientDescentRootFinder(java.util.List<Term> functions,
                                 java.util.Map<Variable,Term> startingPoint)

Creates a new root finder for the given starting point and the given (multi-dimensional) function

Parameters:

startingPoint -

Method Detail

randomRoot

public java.util.Map<Variable,Term> randomRoot()
                                        throws GeneralMathException

Description copied from class: RootFinder

Determines the values for the variables appearing in the function such the function evaluates to zero.

Specified by:

randomRoot in class OptimizationRootFinder

Returns:

a map from variables to terms such that "function" evaluates to zero.

Throws:

GeneralMathException - if something went wrong.