Singular value decomposition for mlx arrays

Note that mlx's svd returns "full" SVD, with U and V' both square matrices. This is different from R's implementation.

Usage

# S3 method for class 'mlx'
svd(x, nu = min(n, p), nv = min(n, p), ...)

Arguments

x

An mlx matrix (2-dimensional array).

nu

Number of left singular vectors to return (0 or min(dim(x))).

nv

Number of right singular vectors to return (0 or min(dim(x))).

...

Additional arguments (unused).

Value

A list with components d, u, and v.

See also

mlx.linalg.svd

Examples

x <- as_mlx(matrix(c(1, 0, 0, 2), 2, 2))
svd(x)
#> $d
#> mlx array [2]
#>   dtype: float32
#>   device: gpu
#>   values:
#> [1] 2 1
#> 
#> $u
#> mlx array [2 x 2]
#>   dtype: float32
#>   device: gpu
#>   values:
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    1    0
#> 
#> $v
#> mlx array [2 x 2]
#>   dtype: float32
#>   device: gpu
#>   values:
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    1    0
#>