svds.sparseLRMatrix: Truncated singular value decomposition of a matrix

In sparseLRMatrix: Represent and Use Sparse + Low Rank Matrices

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 RSpectra::svds().
...	Passed to RSpectra::svds().

Value

A list with the following components:

d	A vector of the computed singular values.
u	An m by nu matrix whose columns contain the left singular vectors. If nu == 0, NULL will be returned.
v	An n by nv matrix whose columns contain the right singular vectors. If nv == 0, NULL will be returned.
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)

sparseLRMatrix documentation built on March 2, 2021, 5:08 p.m.