# Singular Value Decomposition

### Description

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

### Usage

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))
``` |

### Arguments

`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. |

### Details

Extensions of R linear algebra functions.

### Value

`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.

### Examples

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