R: (Generalized) Triangular Decomposition of a Matrix

lu {Matrix}

R Documentation

(Generalized) Triangular Decomposition of a Matrix

Description

Computes (generalized) triangular decompositions of square and other dense matrices.

Usage

lu(x, ...)
## S4 method for signature 'matrix'
lu(x, warnSing = TRUE, ...)
## S4 method for signature 'dgeMatrix'
lu(x, warnSing = TRUE, ...)
## S4 method for signature 'dgCMatrix'
lu(x, errSing = TRUE, order = TRUE, tol = 1, ...)

Arguments

`x`	a dense or sparse matrix, in the latter case of square dimension. No missing values or IEEE special values are allowed.
`warnSing`	(when `x` is a `"denseMatrix"`) logical specifying if a `warning` should be signalled when `x` is singular.
`errSing`	(when `x` is a `"sparseMatrix"`) logical specifying if an error (see `stop`) should be signalled when `x` is singular. When `x` is singular, `lu(x, errSing=FALSE)` returns `NA` instead of an LU decomposition. No warning is signalled and the useR should be careful in that case.
`order`	logical or integer, used to chose which will-reducing permutation technique will be used internally. Do not change unless you know what you are doing.
`tol`	positive number indicating the pivoting tolerance used in `cs_lu`. Do only change with much care.
`...`	further arguments passed to or from other methods.

Details

lu() is a generic function with special methods for different types of matrices. Use showMethods("lu") to list all the methods for the lu generic.

The method for class dgeMatrix (and all dense matrices) is based on LAPACK's "dgetrf" subroutine. It returns a decomposition also for singular and non-square matrices.

The method for class dgCMatrix (and all sparse matrices) is based on functions from the CSparse library. It signals an error (or returns NA, when errSing = FALSE, see above) when the decomposition algorithm fails, as when x is (too close to) singular.

Value

An object of class "LU", i.e., "denseLU" (see its separate help page), or "sparseLU", see sparseLU; this is a representation of a triangular decomposition of x.

References

Golub, G., and Van Loan, C. F. (1989). Matrix Computations, 2nd edition, Johns Hopkins, Baltimore.

Tim Davis (2005) http://www.cise.ufl.edu/research/sparse/CSparse/

Timothy A. Davis (2006) Direct Methods for Sparse Linear Systems, SIAM Series “Fundamentals of Algorithms”.

Examples


##--- Dense  -------------------------
x <- Matrix(rnorm(9), 3, 3)
lu(x)
dim(x2 <- round(10 * x[,-3]))# non-square
expand(lu2 <- lu(x2))

##--- Sparse (see more in ?"sparseLU-class")----- % ./sparseLU-class.Rd

pm <- as(readMM(system.file("external/pores_1.mtx",
                            package = "Matrix")),
         "CsparseMatrix")
str(pmLU <- lu(pm))		# p is a 0-based permutation of the rows
                                # q is a 0-based permutation of the columns
## permute rows and columns of original matrix
ppm <- pm[pmLU@p + 1L, pmLU@q + 1L]
pLU <- drop0(pmLU@L %*% pmLU@U) # L %*% U -- dropping extra zeros
## equal up to "rounding"
ppm[1:14, 1:5]
pLU[1:14, 1:5]

[Package Matrix version 1.0-6 Index]