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Adaptive SVD with Automatic Backend Selection

Source: R/RcppExports.R
adaptive_svd.Rd

Automatically selects the SVD algorithm from the matrix dimensions:

Usage

adaptive_svd(A, k = 0L, method = "auto")

Arguments

A

Matrix (m x n). Input matrix.

k

Integer. Target rank. If 0 (default), computes full SVD.

method

Character. Force a specific method: "auto" (default), "rsvd", "accelerate", "eigen".

Value

A list with components U (m x k), d (k), V (n x k), plus method_used and time_ms.

Details

  • Randomised SVD (Halko et al., 2011): when target rank k << min(m, n)

  • Apple Accelerate / LAPACK: a full dense decomposition otherwise

  • Eigen BDCSVD: cross-platform divide-and-conquer fallback

Examples

set.seed(1L)
A <- matrix(rnorm(20 * 12), nrow = 20, ncol = 12)
res <- adaptive_svd(A, k = 5L, method = "auto")
length(res$d)
#> [1] 5
is.character(res$method_used)
#> [1] TRUE