sla.svd {RScaLAPACK} | R Documentation |
Compute the singular-value decomposition of a rectangular matrix.
sla.svd(A, nu=NULL, nv=NULL, NPROWS=0, NPCOLS=0, MB=16, RFLAG=1, SPAWN=1)
A |
A matrix whose SVD decomposition is to be computed |
nu |
The number of left singular vectors to be computed. Must be either |
nv |
The number of right singular vectors to be computed. Must be either |
NPROWS |
Number of Process Rows in the Process Grid. |
NPCOLS |
Number of Process Cols in the Process Grid. |
MB |
Block Size. |
RFLAG |
Flag saying whether the Process Grid should be released after computation. |
SPAWN |
Flag saying whether a new Process Grid should be spawned. |
If the number of processor rows and columns are both zero, one processor is used. If the number of processor rows is nonzero and the number of processor columns is zero, then the processor rows is taken to be a number of processors, and a grid is made accordingly.
Nagiza Samatova (samatovan@ornl.gov), Guruprasad Kora (koragh@ornl.gov), Srikanth Yoginath (yoginathsb@ornl.gov), David Bauer (bauerda@ornl.gov)
http://www.netlib.org/scalapack/
http://mathworld.wolfram.com/SingularValueDecomposition.html
sla.solve
the SCALAPACK version of the R function solve
sla.qr
the SCALAPACK version of the R function qr
La.svd
the LAPACK implementation of singular value decomposition.
library(RScaLAPACK) rnorm(64*64)->x dim(x)=c(64,64) sla.svd(x, NPROWS=2, NPCOLS=2, MB=16)