Description Usage Arguments Details Value Examples

SVD for distributed matrices with R-like syntax, with calculations performed by the PBLAS and ScaLAPACK libraries.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## 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 non-transposed. 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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
spmd.code = "
library(pbdDMAT, quiet = TRUE)
init.grid()
# don't do this in production code
x <- matrix(1:9, 3)
x <- as.ddmatrix(x)
y <- svd(A)
y
finalize()
"
pbdMPI::execmpi(spmd.code = spmd.code, nranks = 2L)
``` |

pbdDMAT documentation built on Dec. 11, 2018, 5:04 p.m.

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