svds.sparseLRMatrix {sparseLRMatrix} | R Documentation |
Truncated singular value decomposition of a matrix
Description
A thin wrapper around RSpectra::svds()
, please see more detailed
documentation there. In particular, this function leverages the
function interface.
Usage
## S3 method for class 'sparseLRMatrix'
svds(A, k, nu = k, nv = k, opts = list(), ...)
Arguments
A |
Matrix to decompose. |
k |
Number of singular values to estimate. |
nu |
Number of left singular vectors to estimate. |
nv |
Number of right singular vectors to estimate. |
opts |
Passed to |
... |
Passed to |
Value
A list with the following components:
d |
A vector of the computed singular values. |
u |
An |
v |
An |
nconv |
Number of converged singular values. |
niter |
Number of iterations used. |
nops |
Number of matrix-vector multiplications used. |
Examples
set.seed(528491)
n <- 50
m <- 40
k <- 3
A <- rsparsematrix(n, m, 0.1)
U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)
X <- sparseLRMatrix(sparse = A, U = U, V = V)
svds(X, 5)
[Package sparseLRMatrix version 0.1.0 Index]