runExactSVD {BiocSingular} | R Documentation

## Exact SVD

### Description

Perform an exact singular value decomposition.

### Usage

    runExactSVD(x, k=min(dim(x)), nu=k, nv=k, center=FALSE, scale=FALSE,
        deferred=FALSE, fold=Inf, BPPARAM=SerialParam())

### Arguments

`x` | A numeric matrix-like object to use in the SVD.
`k` | Integer scalar specifying the number of singular values to return.
`nu` | Integer scalar specifying the number of left singular vectors to return.
`nv` | Integer scalar specifying the number of right singular vectors to return.
`center` | A logical scalar indicating whether columns should be centered. Alternatively, a numeric vector or `NULL` - see `?"BiocSingular-options"`.
`scale` | A logical scalar indicating whether columns should be scaled. Alternatively, a numeric vector or `NULL` - see `?"BiocSingular-options"`.
`deferred` | Logical scalar indicating whether centering/scaling should be deferred, see `?"BiocSingular-options"`.
`fold` | Numeric scalar specifying the minimum fold difference between dimensions of `x` to compute the cross-product, see `?"BiocSingular-options"`.
`BPPARAM` | A BiocParallelParam object specifying how parallelization should be performed.

### Details

If any of `k`, `nu` or `nv` exceeds `min(dim(x))`, they will be capped and a warning will be raised. The exception is when they are explicitly set to `Inf`, in which case all singular values/vectors of `x` are returned without any warning.

Note that parallelization via `BPPARAM` is only applied to the calculation of the cross-product. It has no effect for near-square matrices where the SVD is computed directly.

### Value

A list containing:

- `d`, a numeric vector of the first `k` singular values.

- `u`, a numeric matrix with `nrow(x)` rows and `nu` columns. Each column contains a left singular vector.

- `u`, a numeric matrix with `ncol(x)` rows and `nv` columns. Each column contains a right singular vector.

### Author(s)

Aaron Lun

### See Also

`svd` for the underlying algorithm.

### Examples

    a <- matrix(rnorm(100000), ncol=20)
    out <- runExactSVD(a)
    str(out)

[Package BiocSingular version 1.8.1 Index]