jsat.math.optimization

Class IterativelyReweightedLeastSquares

java.lang.Object

jsat.math.optimization.IterativelyReweightedLeastSquares

All Implemented Interfaces:

Serializable, Optimizer

public class IterativelyReweightedLeastSquares
extends Object
implements Optimizer

Provides an implementation of the Iteratively Reweighted Least Squares (IRLS) algorithm for solving certain classes of optimization problems. See Wikepida for more information.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
IterativelyReweightedLeastSquares()

Method Summary

Modifier and Type	Method and Description
Vec	optimize(double eps, int iterationLimit, Function f, Function fd, Vec vars, List<Vec> inputs, Vec outputs) Performs optimization on the given inputs to find the minima of the function.
Vec	optimize(double eps, int iterationLimit, Function f, Function fd, Vec vars, List<Vec> inputs, Vec outputs, ExecutorService threadpool) Performs optimization on the given inputs to find the minima of the function.

Methods inherited from class java.lang.Object

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

Constructor Detail

IterativelyReweightedLeastSquares

public IterativelyReweightedLeastSquares()

Method Detail

optimize

public Vec optimize(double eps,
                    int iterationLimit,
                    Function f,
                    Function fd,
                    Vec vars,
                    List<Vec> inputs,
                    Vec outputs)

Description copied from interface: Optimizer

Performs optimization on the given inputs to find the minima of the function. Not all optimization functions require all the parameters to work, and may except some null values.

Specified by:

optimize in interface Optimizer

Parameters:

eps - the desired accuracy of the result.

iterationLimit - the maximum number of iteration steps to allow. This value must be positive

f - the function to optimize. This value can not be null

fd - the derivative of the function to optimize

vars - contains the initial estimate of the minima. The length should be equal to the number of variables being solved for. This value may be altered. This value can not be null.

inputs - a list of input data point values to learn from

outputs - a vector containing the true values for each data point in inputs

Returns:

the computed value for the optimization.

optimize

public Vec optimize(double eps,
                    int iterationLimit,
                    Function f,
                    Function fd,
                    Vec vars,
                    List<Vec> inputs,
                    Vec outputs,
                    ExecutorService threadpool)

Description copied from interface: Optimizer

Performs optimization on the given inputs to find the minima of the function. Not all optimization functions require all the parameters to work, and may except some null values.

Specified by:

optimize in interface Optimizer

Parameters:

eps - the desired accuracy of the result.

iterationLimit - the maximum number of iteration steps to allow. This value must be positive

f - the function to optimize. This value can not be null

fd - the derivative of the function to optimize

vars - contains the initial estimate of the minima. The length should be equal to the number of variables being solved for. This value may be altered. This value can not be null.

inputs - a list of input data point values to learn from

outputs - a vector containing the true values for each data point in inputs

threadpool - a source of threads to perform computation with

Returns:

the computed value for the optimization.