Computes the preprocessing decomposition for the generalized SVD.
Computes the generalized SVD of two upper triangular or trapezoidal matrices.
This topic describes LAPACK computational routines for computing the cosine-sine decomposition (CS decomposition) of a partitioned unitary/orthogonal matrix. The algorithm computes a complete 2-by-2 CS decomposition, which requires simultaneous diagonalization of all the four blocks of a unitary/orthogonal matrix partitioned into a 2-by-2 block structure.
Computes the CS decomposition of an orthogonal/unitary matrix in bidiagonal-block form.
Simultaneously bidiagonalizes the blocks of a partitioned orthogonal/unitary matrix.
Each of the LAPACK driver routines solves a complete problem. To arrive at the solution, driver routines typically call a sequence of appropriate computational routines.
Driver routines are described in the following topics :
Uses QR or LQ factorization to solve a overdetermined or underdetermined linear system with full rank matrix.
Computes the minimum-norm solution to a linear least squares problem using a complete orthogonal factorization of A.
Computes the minimum-norm solution to a linear least squares problem using the singular value decomposition of A.