svd {base}R Documentation

Singular Value Decomposition of a Matrix


Compute the singular-value decomposition of a rectangular matrix.


svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE)

La.svd(x, nu = min(n, p), nv = min(n, p),
       method = c("dgesdd", "dgesvd"))


x a matrix whose SVD decomposition is to be computed.
nu the number of left singular vectors to be computed. This must be one of 0, nrow(x) and ncol(x), except for method = "dgesdd".
nv the number of right singular vectors to be computed. This must be one of 0 and ncol(x).
LINPACK logical. Should LINPACK be used (for compatibility with R < 1.7.0)?
method The LAPACK routine to use in the real case.


The singular value decomposition plays an important role in many statistical techniques. svd and La.svd provide two slightly different interfaces. The main functions used are the LAPACK routines DGESDD and ZGESVD; svd(LINPACK=TRUE) provides an interface to the LINPACK routine DSVDC, purely for backwards compatibility.

La.svd provides an interface to both the LAPACK routines DGESVD and DGESDD. The latter is usually substantially faster if singular vectors are required: see Most benefit is seen with an optimized BLAS system. Using method="dgesdd" requires IEEE 754 arithmetic. Should this not be supported on your platform, method="dgesvd" is used, with a warning.

Computing the singular vectors is the slow part for large matrices.

Unsuccessful results from the underlying LAPACK code will result in an error giving a positive error code: these can only be interpreted by detailed study of the FORTRAN code.


The SVD decomposition of the matrix as computed by LINPACK,

X = U D V',

where U and V are orthogonal, V' means V transposed, and D is a diagonal matrix with the singular values D[i,i]. Equivalently, D = U' X V, which is verified in the examples, below.
The returned value is a list with components

d a vector containing the singular values of x.
u a matrix whose columns contain the left singular vectors of x, present if nu > 0
v a matrix whose columns contain the right singular vectors of x, present if nv > 0.

For La.svd the return value replaces v by vt, the (conjugated if complex) transpose of v.


Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Dongarra, J. J., Bunch, J. R., Moler, C. B. and Stewart, G. W. (1978) LINPACK Users Guide. Philadelphia: SIAM Publications.

Anderson. E. and ten others (1999) LAPACK Users' Guide. Third Edition. SIAM.
Available on-line at

See Also

eigen, qr.

capabilities to test for IEEE 754 arithmetic.


hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") }
X <- hilbert(9)[,1:6]
(s <- svd(X))
D <- diag(s$d)
s$u %*% D %*% t(s$v) #  X = U D V'
t(s$u) %*% X %*% s$v #  D = U' X V

[Package base version 2.1.0 Index]