mikera.matrixx.decompose.impl.chol

Class CholeskyCommon

java.lang.Object

mikera.matrixx.decompose.impl.chol.CholeskyCommon

Direct Known Subclasses:

Cholesky, CholeskyInner

public abstract class CholeskyCommon
extends Object

This is an abstract class for a Cholesky decomposition. It provides the solvers, but the actual decompsoition is provided in other classes.

A Cholesky Decomposition is a special decomposition for positive-definite symmetric matrices that is more efficient than other general purposes decomposition. It refactors matrices using one of the two following equations:

L*L^T=A
R^T*R=A

where L is a lower triangular matrix and R is an upper traingular matrix.

Author:

Peter Abeles

See Also:

CholeskyDecompositionInner, org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionBlock, org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL

Field Summary

Fields

Modifier and Type	Field and Description
protected int	n
protected double[]	t
protected Matrix	T
protected double[]	vv

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	CholeskyCommon() Creates a CholeksyDecomposition capable of decomposing a matrix that is n by n, where n is the width.

Method Summary

Modifier and Type	Method and Description
protected ICholeskyResult	_decompose(AMatrix mat) Performs Choleksy decomposition on the provided matrix.
protected abstract CholeskyResult	decomposeLower() Performs an lower triangular decomposition.

Methods inherited from class java.lang.Object

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

Field Detail

n

protected int n

T

protected Matrix T

t

protected double[] t

vv

protected double[] vv

Constructor Detail

CholeskyCommon

protected CholeskyCommon()

Creates a CholeksyDecomposition capable of decomposing a matrix that is n by n, where n is the width.

Method Detail

_decompose

protected ICholeskyResult _decompose(AMatrix mat)

Performs Choleksy decomposition on the provided matrix.

If the matrix is not positive definite then this function will return null since it can't complete its computations. Not all errors will be found. This is an efficient way to check for positive definiteness.

Parameters:

mat - A symmetric positive definite matrix.

Returns:

ICholeskyResult if decomposition is successful, null otherwise.

decomposeLower

protected abstract CholeskyResult decomposeLower()

Performs an lower triangular decomposition.