BunchKaufman-methods {Matrix}

R Documentation

Bunch-Kaufman Decomposition Methods

Description

The Bunch-Kaufman Decomposition of a square symmetric matrix A is A = P LDL' P' where P is a permutation matrix, L is unit-lower triangular and D is block-diagonal with blocks of dimension 1\times 1 or 2\times2.

This is generalization of a pivoting LDL' Cholesky decomposition.

Usage

## S4 method for signature 'dsyMatrix'
BunchKaufman(x, ...)
## S4 method for signature 'dspMatrix'
BunchKaufman(x, ...)
## S4 method for signature 'matrix'
BunchKaufman(x, uplo = NULL, ...)

Arguments

x	a symmetric square matrix.
uplo	optional string, "U" or "L" indicating which “triangle” half of x should determine the result. The default is "U" unless x has a uplo slot which is the case for those inheriting from class symmetricMatrix, where x@uplo will be used.
...	potentially further arguments passed to methods.

Details

FIXME: We really need an expand() method in order to work with the result!

Value

an object of class BunchKaufman, which can also be used as a (triangular) matrix directly. Somewhat amazingly, it inherits its uplo slot from x.

Methods

Currently, only methods for dense numeric symmetric matrices are implemented. To compute the Bunch-Kaufman decomposition, the methods use either one of two Lapack routines:

x = "dspMatrix"

routine dsptrf(); whereas

x = "dsyMatrix"

, and

x = "matrix"

use dsytrf().

References

The original LAPACK source code, including documentation; https://netlib.org/lapack/double/dsytrf.f and https://netlib.org/lapack/double/dsptrf.f

See Also

The resulting class, BunchKaufman. Related decompositions are the LU, lu, and the Cholesky, chol (and for sparse matrices, Cholesky).

Examples

data(CAex)
dim(CAex)
isSymmetric(CAex)# TRUE
CAs <- as(CAex, "symmetricMatrix")
if(FALSE) # no method defined yet for *sparse* :
   bk. <- BunchKaufman(CAs)
## does apply to *dense* symmetric matrices:
bkCA <- BunchKaufman(as(CAs, "denseMatrix"))
bkCA
pkCA <- pack(bkCA)
stopifnot(is(bkCA, "triangularMatrix"),
          is(pkCA, "triangularMatrix"),
          is(pkCA, "packedMatrix"))

image(bkCA)# shows how sparse it is, too
str(R.CA <- as(bkCA, "sparseMatrix"))
## an upper triangular 72x72 matrix with only 144 non-zero entries
stopifnot(is(R.CA, "triangularMatrix"), is(R.CA, "CsparseMatrix"))

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