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), ..., device = NULL)

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; ignored.
device: Execution target for APIs that expose a one-off device or stream override. Supply "gpu", "cpu", or an mlx_stream created via mlx_new_stream(). Ordinary array operations use the current mlx_device() instead.

Value

A list with components d, u, and v.

Details

As of MLX 0.31.1, this operation only runs on CPU. Run it inside with_device() or local_device(), or pass device = "cpu".

Examples

x <- mlx_matrix(c(1, 0, 0, 2), 2, 2)
svd(x, device = "cpu")
#> $d
#> mlx array [2]
#>   dtype: float32
#>   values:
#> [1] 2 1
#> 
#> $u
#> mlx array [2 x 2]
#>   dtype: float32
#>   values:
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    1    0
#> 
#> $v
#> mlx array [2 x 2]
#>   dtype: float32
#>   values:
#>      [,1] [,2]
#> [1,]    0    1
#> [2,]    1    0
#>