# chol: Cholesky Decomposition using GPU or multi-core CPU In HiPLARM: High Performance Linear Algebra in R

## Description

Compute the Cholesky factorization of a real symmetric positive-definite square matrix using GPU or multi-core CPU.

## Usage

 ```1 2 3``` ```chol(x, ...) ## S4 method for signature 'dpoMatrix' chol(x, pivot = FALSE, ...) ```

## Arguments

 `x` if `x` is not positive definite, an error is signalled. `pivot` logical indicating if pivoting is used. This is not supported for the GPU implementation `...` potentially further arguments passed to methods.

## Details

For further details on classes and methods see the full Matrix package documentation.

## Methods

chol

`signature(x = "dgeMatrix")`: works via `"dpoMatrix"`

chol

`signature(x = "dpoMatrix")`: Returns (and stores) the Cholesky decomposition of `x`, via LAPACK routines `dlacpy` and either `magma_dpotrf` or `PLASMA_dpotrf`.

## References

Martin Maechler, Douglas Bates (Matrix package)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```showMethods(chol, inherited = FALSE) # show different methods sy2 <- new("dsyMatrix", Dim = as.integer(c(2,2)), x = c(14, NA,32,77)) (c2 <- chol(sy2))#-> "Cholesky" matrix stopifnot(all.equal(c2, chol(as(sy2, "dpoMatrix")), tol= 1e-13)) str(c2) ## An example where chol() can't work (sy3 <- new("dsyMatrix", Dim = as.integer(c(2,2)), x = c(14, -1, 2, -7))) try(chol(sy3)) # error, since it is not positive definite ```

HiPLARM documentation built on May 29, 2017, 10:42 p.m.