Re: Who uses clapack?

From: Bill Shortall (pecos_at_cminet.net)
Date: 12/10/04 

Date: Fri, 10 Dec 2004 13:36:44 -0700

"Victor Eijkhout" <see.sig@for.addy> wrote in message
news:1gol29n.2hz9tynnu0bN%see.sig@for.addy...
> Bill Shortall <pecos@cminet.net> wrote:
>
> > For the last few years I have been working on a
> > set of C++ classes to do linear algebra. I was trying to
> > acheive most of the functionality of Lapack/Blas but
> > in a very user friendly fashion. The classes center
> > arround a general purpose vector class along with a dense rectangular
Matrix
> > class and then some more compilcated classes like a vector of vectors
and
> > vector of matrices.
>
> Bill,
>
> please mail me a pointer to your software.
>
> However, without wanting to be disrespectful of your software, I wonder
> if this is such a wise approach. Lapack is a high quality code base that
> supports several data formats: dense, symmetric, banded, tridiagonal.
> There is use for that, so before you can state
>
> > Once you've used it
> > you will never go back to LAPACK
>
> you have to duplicate a lot of that functionality.
>
> Then, Lapack has (especially in the eigenvalue part) state of the art
> algorithms. The people coming up with the best algorithms are
> implementing them straight in Lapack. What eigenvalue algorithms do you
> use? Condition estimation, iterative refinement, et cetera.
>
> V.
> --
> email: lastname at cs utk edu
> homepage: www cs utk edu tilde lastname

Hi Victor,
  I need a e-mail address to send you the algebra package
its not on the internet
do you want the microsoft VC6 or the the Linux GCC 2.7
version. The package is about 750 kilobytes. I wonder
how big Lapack/Blas is ?
   Sure the Lapack people might have some more sophisticated algorithms but
only a few problems need them. The advantages of working in C++ and having
all its tremendous libraries available to you are overwhelming.
   The eigenvalue methods I use are the old standbys
reduction to either tri or bi diagonal then QR iteration. For
real general matrices I use reduction to hessian then QR iteration for the
eigenvalues and inverse iteration for the eigenvectors.

Relevant Pages

  • Re: Who uses clapack?
    ... >> you will never go back to LAPACK ... The people coming up with the best algorithms are ... What eigenvalue algorithms do you ... reduction to either tri or bi diagonal then QR iteration. ...
    (sci.math.num-analysis)
  • Re: Who uses clapack?
    ... >> you will never go back to LAPACK ... The people coming up with the best algorithms are ... What eigenvalue algorithms do you ... reduction to either tri or bi diagonal then QR iteration. ...
    (comp.lang.c)
  • Re: Eigenvalues of tridiagonal matrix
    ... eigen-values/vectors and matrix exponent of the following tridiagonal matrix ... There seems to be a lot of good stuff on eigenvalue ... algorithms at the LAPACK section of NETLIB. ...
    (sci.math)
  • Re: Help please..
    ... likely you will find faster general-purpose implementations ... than LAPACK on general-purpose platforms. ... compile the source code for your target. ... ESPRIT algorithms some ten years ago. ...
    (comp.dsp)
  • Re: Is there more stable mumerical package to calculate the condition number ?
    ... My problem is to calculate the condition number of arbitrary matrix A. ... When the largest eigenvalue and the smallest eigenvalue of A are given by ... This is why I require a numerical package. ... Eigen-decomposition which LAPACK provides. ...
    (sci.math.num-analysis)
Loading