Module svd 

low level implementation of the svd of a matrix

the svd of a matrix $A$ 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:

$$A = U S V^H$$

Modules

bidiag

bidiagonalization

Structs

SvdParams

svd tuning parameters

Enums

ComputeSvdVectors

whether the singular vectors should be computed

SvdError

svd error

Functions

pseudoinverse_from_svd

computes a self-adjoint matrix’s pseudoinverse, given the svd factors $S$, $U$ and $V$

pseudoinverse_from_svd_scratch

computes the size and alignment of the workspace required to compute a matrix’s pseudoinverse, given the svd

pseudoinverse_from_svd_with_tolerance

computes a self-adjoint matrix’s pseudoinverse, given the svd factors $S$, $U$ and $V$, and tolerance parameters for determining zero singular values

svd

computes the svd of $A$, with the singular vectors being omitted, thin or full

svd_scratch

computes the size and alignment of the workspace required to compute a matrix’s svd