Singular Value Decomposition Solvers - SVD

slepc-3.19.2 2023-09-05
Report Typos and Errors

## Singular Value Decomposition Solvers - SVD: Examples Complex Networks

The Singular Value Decomposition Solver (SVD) is very similar to the EPS object, but intended for the computation of the partial SVD of a rectangular matrix. With this type of object, the user can specify an SVD problem and solve it with any of the different solvers encapsulated by the package. Some of these solvers are actually implemented through calls to EPS eigensolvers.

The user interface is very similar to that of EPS, both for the options database (e.g., `-svd_nsv 4 -svd_type lanczos`), and for the programmatic interface (e.g., SVDSetDimensions() / SVDSetType()).

ex8.c: Estimates the 2-norm condition number of a matrix A, that is, the ratio of the largest to the smallest singular values of A
ex14.c: Solves a singular value problem with the matrix loaded from a file
ex15.c: Singular value decomposition of the Lauchli matrix
ex15f.F: Singular value decomposition of the Lauchli matrix.
ex45.c: Computes a partial generalized singular value decomposition (GSVD)
ex48.c: Solves a GSVD problem with matrices loaded from a file
ex51.c: Computes a partial GSVD of two matrices from IR Tools example
ex52.c: Partial hyperbolic singular value decomposition (HSVD) from a file
ex53.c: Partial hyperbolic singular value decomposition (HSVD) of matrix generated by plane rotations