parJacobi: SVD using Jacobi algorithm.
In isglobal-brge/svdParallel: Parallel implementation of SVD
Description
Usage
Arguments
Details
Value
Examples
Description
SVD using parallel Jacobi algorithm
Usage
1
Arguments
x
a real symmetric matrix
tol
a small positive error tolerance. Default is machine tolerance
Details
Eigenvalues and eigenvectores of a real symmetric matrix using
two-sided Jacobi algorithm in parallel.
It needs doParallel library for parallelization.
Value
a list of two components as for base::eigen
Examples
1
2
3
4
5
6
(V <- crossprod(matrix(1:25, 5)))
ncores <- detectCores() - 1
registerDoParallel(cores=ncores)
cl <- makeCluster(ncores)
parJacobi(V)
stopCluster(cl)
isglobal-brge/svdParallel documentation built on June 26, 2019, 9:40 p.m.
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Description Usage Arguments Details Value Examples
Description
SVD using parallel Jacobi algorithm
Usage
1 |
Arguments
x |
a real symmetric matrix |
tol |
a small positive error tolerance. Default is machine tolerance |
Details
Eigenvalues and eigenvectores of a real symmetric matrix using two-sided Jacobi algorithm in parallel. It needs doParallel library for parallelization.
Value
a list of two components as for base::eigen
Examples
1 2 3 4 5 6 | (V <- crossprod(matrix(1:25, 5)))
ncores <- detectCores() - 1
registerDoParallel(cores=ncores)
cl <- makeCluster(ncores)
parJacobi(V)
stopCluster(cl)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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