LAPACK (Linear Algebra PACKage) provides routines for solving systems of simultaneous linear equations, linear least-squares problems, eigenvalue problems, and singular value problems. Routines for the associated matrix factorizations are also provided, as are related computations such as reordering Schur factorizations, condition number estimation and forward and backward error estimation. LAPACK is intended for dense and banded matrices, but not general sparse matrices. In all areas, similar functionality is provided for real and complex matrices, in both single and double precision.
NAG now provides example programs to illustrate the use of LAPACK. Most users will already have LAPACK available, either a version they have installed themselves or a vendor version. Alternatively, LAPACK downloads are available at http://www.netlib.org/lapack/.
We provide an example of the use of most double precision LAPACK routines, both driver and computational routines. Each example comprises a description of the problem, together with links to the example program, the example data and example results. Non-LAPACK routines, mainly matrix printing routines, are also needed to run the programs, and these are provided.
The LAPACK example programs and associated material can be freely downloaded from the NAG GitHub page.
The original goal of the LAPACK project was to make the widely used EISPACK and LINPACK libraries run efficiently on shared-memory vector and parallel processors. NAG is proud to have provided two of the contributors to the LAPACK project. Jeremy Du Croz has since retired, but NAG Principal Consultant, Sven Hammarling, is still active in the project. Because Sven is still actively involved in the project, NAG is able to ensure that the latest code is included, complete with error corrections, and has provided LAPACK example programs for the benefit of LAPACK users.