irlba {irlba}  R Documentation 
The augmented implicitly restarted Lanczos bidiagonalization (IRLBA) algorithm finds a few approximate singular values and corresponding singular vectors of a matrix using a method of Baglama and Reichel. It is a fast and memoryefficient way to compute a partial SVD.
irlba(A, nu = 5, nv = 5, adjust = 3, aug = c("ritz","harm"), sigma = c("ls","ss"), maxit = 1000, m_b = 20, reorth = 2, tol = 1e06, V = NULL, matmul = NULL)
A 
A doubleprecision real or complex matrix or real sparse matrix 
nu 
Number of desired left singular vectors 
nv 
Number of desired right singular vectors 
adjust 
Number of extra approximate singular values to compute to enhance convergence 
aug 
"ritz" for Ritz "harm" for harmonic Ritz vector augmentation 
sigma 
"ls" for largest few singular values, "ss" for smallest 
maxit 
Maximum number of iterations 
m_b 
Size of the projected bidiagonal matrix 
reorth 
Either 1 or 2: full (2) or onesided (1) reorthogonalization 
tol 
Convergence is determined when A*V  U*S <= tol*A, where A is approximated by the largest singular value of all projection matrices. 
V 
Optional matrix of approximate right singular vectors 
matmul 
Option matrix multiply function–if specified, matmul must
be a function that takes three arguments:

The syntax of irlba
largely conforms to svd
, with an
important exception. The usual R svd
function always returns a complete
set of singular values, even if the number of singular vectors nu
or nv
is set less than the maximum. The irlba
function
returns a number of singular values equal to the maximum of the
number of specified singular vectors nu
and nv
.
d 
max (nu, nv) approximate singular values 
u 
nu approximate left singular vectors 
v 
nv approximate right singular vectors 
iter 
The number of Lanczos iterations carried out 
mprod 
The total number of matrix vector products carried out 
Adapted for R by B. W. Lewis <blewis@illposed.net>
"Augmented Implicitly Restarted Lanczos Bidiagonalization Methods", J. Baglama and L. Reichel, SIAM J. Sci. Comput. 2005.
A < matrix(runif(100*100),100,100) S < irlba (A) S$d