Matrix (version 1.2-8)

CHMfactor-class: CHOLMOD-based Cholesky Factorizations

Description

The virtual class "CHMfactor" is a class of CHOLMOD-based Cholesky factorizations of symmetric, sparse, compressed, column-oriented matrices. Such a factorization is simplicial (virtual class "CHMsimpl") or supernodal (virtual class "CHMsuper"). Objects that inherit from these classes are either numeric factorizations (classes "dCHMsimpl" and "dCHMsuper") or symbolic factorizations (classes "nCHMsimpl" and "nCHMsuper").

Usage

isLDL(x)
"update"(object, parent, mult = 0, ...)
.updateCHMfactor(object, parent, mult)
## and many more methods, notably,
##   solve(a, b, system = c("A","LDLt","LD","DLt","L","Lt","D","P","Pt"), ...)
##   -----    see below

Arguments

x,object,a

a "CHMfactor" object (almost always the result of Cholesky()).

parent

a "dsCMatrix" or "dgCMatrix" matrix object with the same nonzero pattern as the matrix that generated object. If parent is symmetric, of class "dsCMatrix", then object should be a decomposition of a matrix with the same nonzero pattern as parent. If parent is not symmetric then object should be the decomposition of a matrix with the same nonzero pattern as tcrossprod(parent).

Since Matrix version 1.0-8, other "sparseMatrix" matrices are coerced to dsparseMatrix and CsparseMatrix if needed.

mult

a numeric scalar (default 0). mult times the identity matrix is (implicitly) added to parent or tcrossprod(parent) before updating the decomposition object.

...

potentially further arguments to the methods.

Objects from the Class

Objects can be created by calls of the form new("dCHMsuper", ...) but are more commonly created via Cholesky(), applied to dsCMatrix or lsCMatrix objects. For an introduction, it may be helpful to look at the expand() method and examples below.

Slots

of "CHMfactor" and all classes inheriting from it:

Slots of the non virtual classes “[dl]CHM(super|simpl)”:

Methods

See Also

Cholesky, also for examples; class dgCMatrix.

Examples

## An example for the expand() method
n <- 1000; m <- 200; nnz <- 2000
set.seed(1)
M1 <- spMatrix(n, m,
               i = sample(n, nnz, replace = TRUE),
               j = sample(m, nnz, replace = TRUE),
               x = round(rnorm(nnz),1))
XX <- crossprod(M1) ## = M1'M1  = M M'  where M <- t(M1)
CX <- Cholesky(XX)
isLDL(CX)
str(CX) ## a "dCHMsimpl" object
r <- expand(CX)
L.P <- with(r, crossprod(L,P))  ## == L'P
PLLP <- crossprod(L.P)          ## == (L'P)' L'P == P'LL'P  = XX = M M'
b <- sample(m)
stopifnot(all.equal(PLLP, XX), 
          all(as.vector(solve(CX, b, system="P" )) == r$P %*% b),
          all(as.vector(solve(CX, b, system="Pt")) == t(r$P) %*% b) )

u1 <- update(CX, XX,    mult=pi)
u2 <- update(CX, t(M1), mult=pi) # with the original M, where XX = M M'
stopifnot(all.equal(u1,u2, tol=1e-14))

   ## [ See  help(Cholesky)  for more examples ]
   ##        -------------