svd: svd
In kazaam: Tools for Tall Distributed Matrices

Description Usage Arguments Details Value Communication Examples

Singular value decomposition.

1 2	## S4 method for signature 'shaq' svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE)

`x`	A shaq.
`nu`	number of left singular vectors to return.
`nv`	number of right singular vectors to return.
`LINPACK`	Ignored.

The factorization works by first forming the crossproduct X^T X and then taking its eigenvalue decomposition. In this case, the square root of the eigenvalues are the singular values. If the left/right singular vectors U or V are desired, then in either case, V is computed (the eigenvectors). From these, U can be reconstructed, since if X = UΣ V^T, then U = XVΣ^{-1}.

A list of elements d, u, and v, as with R's own svd(). The elements are, respectively, a regular vector, a shaq, and a regular matrix.

The operation is completely local except for forming the crossproduct, which is an allreduce() call, quadratic on the number of columns.

## Not run: 
library(kazaam)
x = ranshaq(runif, 10, 3)

svd = svd(x)
comm.print(svd$d) # a globally owned vector
svd$u # a shaq
comm.print(svd$v) # a globally owned matrix

finalize()

## End(Not run)