Module svd

Low level implementation of the SVD of a matrix.

The SVD of a matrix $M$ of shape $(m, n)$ is a decomposition into three components $U$, $S$, and $V$, such that:

• $U$ has shape $(m, m)$ and is a unitary matrix,
• $V$ has shape $(n, n)$ and is a unitary matrix,
• $S$ has shape $(m, n)$ and is zero everywhere except the main diagonal,
• and finally:

$$M = U S V^H.$$

Structs

• SvdParams
  SVD tuning parameters.

Enums

• ComputeVectors
  Indicates whether the singular vectors are fully computed, partially computed, or skipped.

Functions

• compute_svd
  Computes the singular value decomposition of matrix.
• compute_svd_custom_epsilon
  See compute_svd.
• compute_svd_req
  Computes the size and alignment of required workspace for performing a singular value decomposition. $U$ and $V$ may be computed fully, partially, or not computed at all.