LAPACK

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LAPACK
Software Details:
Version: 3.5.0
Upload Date: 17 Feb 15
Distribution Type: Freeware
Downloads: 47

Rating: 3.0/5 (Total Votes: 1)

LAPACK is an open source and portable command-line software that provides linear algebra library written in Fortran77 and designed to provide various routines for solving least-squares solutions of linear systems of equations, systems of simultaneous linear equations, singular value problems, and eigenvalue problems.

These routines are written in such a way that they allow the computation to perform as calls to BLAS (Basic Linear Algebra Subprograms).

The main goal of the LAPACK library is to make the LINPACK and EISPACK libraries run efficiently on parallel and shared-memory vector processors. A Fortran95 interface for the LAPACK library also exists, as well as a C++ version for a subset of LAPACK routines, and a f2c'ed version.

What is new in this release:

  • This version adds xGEQRT, a QR factorization facility that enables better performance when the blocked reflectors need to be reused.
  • It adds xGEQRT3, a recursive QR factorization facility that has high performance on tall and skinny matrices.
  • It adds xTPQRT, a collection of Communication-Avoiding QR sequential kernels.
  • It replaces the build system with CMAKE for better portability.
  • It adds Doxygen documentation.
  • It integrates LAPACKE's C language APIs into LAPACK.

What is new in version 3.3.0:

  • The APIs for C and Fortran have been cleaned up to make it easy to use both.
  • Functions have been added for computing the complete CS decomposition of a partitioned unitary matrix.
  • xSYTRF and xSYTRI have been accelerated.
  • SLAMCH and DLAMCH have been made thread-safe.

What is new in version 3.2.0:

  • Extra-precise iterative refinement. XBLAS.
  • Non-negative diagonals from Householder QR.
  • High-performance QR and Householder Reflections on low-profile matrices.
  • A new fast and accurate Jacobi SVD.
  • Routines for Rectangular Full Packed format.
  • Pivoted Cholesky.
  • Mixed precision iterative refinement routines.
  • Some new variants for one-sided factorization.
  • A more robust DQDS algorithm.
  • Improvements to the multi-shift Hessenberg QR algorithm.

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