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LAPACK is a library of numerical linear algebra subroutines
designed for high performance on workstations, vector computers,
and shared memory multiprocessors. Release 3.0 of LAPACK introduces
new routines and extends the functionality of existing routines.
The most significant new routines and functions include: a faster
singular value decomposition computed by divide-and-conquer; faster
routines for solving rank-deficient least squares problems: using
QR with column pivoting; using the SVD based on divide-and-conquer;
new routines for the generalized symmetric eigenproblem: faster
routines based on divide-and-conquer; routines based on
bisection/inverse iteration, for computing part of the spectrum;
faster routine for the symmetric eigen problem using "relatively
robust eigenvector algorithm"; new simple and expert drivers for
the generalized nonsymmetric eigenproblem, including error bounds;
solver for generalized Sylvester equation, used in computational
routines Each Users' Guide comes with a "Quick Reference Guide"
card.
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