Module linxal::svd

Solve singular value decomposition problems.

The simgular value decomposition of an m x n matrix M is are matrices U, D, and V such that.

M = U * D * V^T

where U is an orthogonal m x m matrix, V is an orthogonal n x n matrix, and D is a real-valued m x n diagonal matrix. The entries along the diagonal of D, called the singular values of M, are eigenvalues of sqrt(M^T * M), and are guaranteed to be real.

Reexports

pub use self::general::SVD;

pub use self::types::{SVDSolution, SVDError, SingularValue};

Modules

general	Solve singular value decomposition (SVD) of arbitrary matrices.
types