R: Randomized Singular Value Decomposition (rsvd).

rsvd {rsvd}

R Documentation

Randomized Singular Value Decomposition (rsvd).

Description

The randomized SVD computes the near-optimal low-rank approximation of a rectangular matrix using a fast probablistic algorithm.

Usage

rsvd(A, k = NULL, nu = NULL, nv = NULL, p = 10, q = 2,
  sdist = "normal")

Arguments

`A`	array_like; a real/complex (m, n) input matrix (or data frame) to be decomposed.
`k`	integer; specifies the target rank of the low-rank decomposition. k should satisfy k << min(m,n).
`nu`	integer, optional; number of left singular vectors to be returned. nu must be between 0 and k.
`nv`	integer, optional; number of right singular vectors to be returned. nv must be between 0 and k.
`p`	integer, optional; oversampling parameter (by default p=10).
`q`	integer, optional; number of additional power iterations (by default q=2).
`sdist`	string c( 'unif', 'normal', 'rademacher'), optional; specifies the sampling distribution of the random test matrix: 'unif' : Uniform '[-1,1]'. 'normal' (default) : Normal '~N(0,1)'. 'rademacher' : Rademacher random variates.

Value

rsvd returns a list containing the following three components:

`d`	array_like; singular values; vector of length (k).
`u`	array_like; left singular vectors; (m, k) or (m, nu) dimensional array.
`v`	array_like; right singular vectors; (n, k) or (n, nv) dimensional array.

Note

The singular vectors are not unique and only defined up to sign (a constant of modulus one in the complex case). If a left singular vector has its sign changed, changing the sign of the corresponding right vector gives an equivalent decomposition.

Author(s)

N. Benjamin Erichson, erichson@uw.edu

References

[1] N. Halko, P. Martinsson, and J. Tropp. "Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions" (2009). (available at arXiv http://arxiv.org/abs/0909.4061).
[2] N. B. Erichson, S. Voronin, S. Brunton, J. N. Kutz. "Randomized matrix decompositions using R" (2016). (available at 'arXiv http://arxiv.org/abs/1608.02148).

Examples

library('rsvd')

# Create a n x n Hilbert matrix of order n,
# with entries H[i,j] = 1 / (i + j + 1).
hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") }
H <- hilbert(n=50)

# Low-rank (k=10) matrix approximation using rsvd
k=10
s <- rsvd(H, k=k)
Hre <- s$u %*% diag(s$d) %*% t(s$v) # matrix approximation
print(100 * norm( H - Hre, 'F') / norm( H,'F')) # percentage error
# Compare to truncated base svd
s <- svd(H)
Hre <- s$u[,1:k] %*% diag(s$d[1:k]) %*% t(s$v[,1:k]) # matrix approximation
print(100 * norm( H - Hre, 'F') / norm( H,'F')) # percentage error

[Package rsvd version 0.9 Index]