Thanks a lot, Igor, for the example and all the work you put into the
library.  Both methods (1) and (2) chug along for a few minutes but end up
spitting out a result that looks reasonable.

Cheers,

Mike


On Tue, Apr 9, 2013 at 12:51 AM, Igor Zhuravlov <[email protected]> wrote:

> On 8 апреля 2013 22:53:13 Michal D. wrote:
> > I haven't been able to sort through the mt code to decipher how to call
> the
> > routines with a single input matrix.  It looks like quite a substantial
> > amount of important work.
>
>    load'math/mt'
>
>    NB. generate random symmetric 4x4-matrix to play with (see rand.ijs)
>    ] A=. _1 1 0 3 _6 4&gemat_mt_ hemat_mt_ 4 4
>  _0.0157367 0.000464677 _0.0096888  _7.10246
> 0.000464677    _1.38946   _11.0957   1.71751
>  _0.0096888    _11.0957  0.0149438  _1.78811
>    _7.10246     1.71751   _1.78811 0.0686757
>
>    NB. case 1: use mt-only staff
>    NB. - since there is no equivalent xGEEV('N','N',...), let's use
>    NB.   equivalent of xGGEV('N','N',...), either ggevunn or ggevlnn
>    NB. - ggevunn is column-wise algo implementing xGGEV('N','N',...) of
> LAPACK
>    NB.   version 3.2.2, ggevlnn is row-wise algo modeling the same
>    NB.   xGGEV('N','N',...)
>
>    NB. 1) create 4x4 identity matrix
>    I=. idmat_mt_ 4
>
>    NB. 2) compose generalized eigenvalue problem and feed it to ggevlnn
>    ggevlnn_mt_ A ,: I
> 11.3293 6.40637 _7.25198 _11.8053
>       1       1        1        1
>    NB. - result is 2x4-matrix of generalized eigenvalues, where 1st row
>    NB.   is A's eigenvalues
>
>    NB. case 2: create new definition to get necessary functionality,
>    NB. see application section for ggevlxx in ev.ijs
>
>    NB. 1) simulate LAPACK's xGEEV('N','N',...):
>    NB. e=. geevlnn A
>    geevlnn=: {.@ggevlnn_mt_@(,:(idmat_mt_@c_mt_))
>
>    NB. 2) feed A to it
>    geevlnn A
> 11.3293 6.40637 _7.25198 _11.8053
>
> --
> WBR
> Igor
> ----------------------------------------------------------------------
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