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Cholesky Decomposition

Source: R/api.R
nv_cholesky.Rd

Computes the Cholesky decomposition of a symmetric positive-definite matrix. Supports batched inputs: dimensions before the last two are batch dimensions.

Usage

nv_cholesky(a, lower = TRUE)

Arguments

a

(tensorish)
Symmetric positive-definite matrix with at least 2 dimensions. The last two dimensions form the square matrix; any leading dimensions are batch dimensions.

lower

(logical(1))
If TRUE (default), compute the lower triangular factor L such that a = L %*% t(L). If FALSE, compute the upper triangular factor U such that a = t(U) %*% U.

Value

tensorish
Triangular matrix with the same shape and data type as the input.

See also

nv_solve(), nvl_cholesky()

Examples

jit_eval({
  a <- nv_tensor(matrix(c(4, 2, 2, 3), nrow = 2), dtype = "f32")
  nv_cholesky(a)
})
#> AnvilTensor
#>  2.0000 0.0000
#>  1.0000 1.4142
#> [ CPUf32{2,2} ]