SVD for distributed matrices with Rlike syntax, with calculations performed by the PBLAS and ScaLAPACK libraries.
1 2 3 4 5 6 7 8 9 10 11  ## S4 method for signature 'ANY'
La.svd(x, nu = min(n, p), nv = min(n, p))
## S4 method for signature 'ddmatrix'
La.svd(x, nu = min(n, p), nv = min(n, p))
## S4 method for signature 'ANY'
svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE)
## S4 method for signature 'ddmatrix'
svd(x, nu = min(n, p), nv = min(n, p))

x 
numeric distributed matrices. 
nu 
number of left singular vectors to return when calculating singular values. 
nv 
number of right singular vectors to return when calculating singular values. 
LINPACK 
Ignored. 
Extensions of R linear algebra functions.
La.svd()
performs singular value decomposition, and returns the
transpose of right singular vectors if any are requested. Singular values
are stored as a global R vector. Left and right singular vectors are unique
up to sign. Sometimes core R (via LAPACK) and ScaLAPACK will disagree as to
what the left/right singular vectors are, but the disagreement is always
only up to sign.
svd()
performs singular value decomposition. Differs from
La.svd()
in that the right singular vectors, if requested, are
returned nontransposed. Singular values are stored as a global R vector.
Sometimes core R (via LAPACK) and ScaLAPACK will disagree as to what the
left/right singular vectors are, but the disagreement is always only up to
sign.
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