From ihaka@stat.auckland.ac.nz  Sun May 18 20:50:16 1997
From: ihaka@stat.auckland.ac.nz (Ross Ihaka)
Date: Mon, 19 May 1997 07:50:16 +1200 (NZST)
Subject: R-alpha: Eigenvalue Computation Query
Message-ID: <199705181950.HAA23297@stat1.stat.auckland.ac.nz>

I have been looking at the "eigen" function and have reintroduced the
ability to compute (right) eigenvalues and vectors for non-symmetric
matrices.  I've also made "eigen" complex capable.

The code is based on the eispack entry points RS, RG, CH, CG (which is
what S appears to use too).  The problem with both the S and R
implementations is that they consume huge amounts of memory.  Some of
this is due to purely ".Fortran" overhead, which I think I can cure.
But some of the bloat is due to the inclusion of special eigenvalues-only
code from eispack.

The question is:

Should I drop this special code and always compute both eigenvalues
and eigenvectors?  This would substantially reduce code size, but might
increase computational cost in the case where only eigenvalues are
needed.


PS: For the lapack fans out there ...  I looked hard at using lapack
as an alternative to eispack, but it's written in a fashion which
does not make its addition to R as simple as linpack and eispack.
When we all have SMP desktop machines it will become more of an issue.
	Ross
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R-alpha: Eigenvalue Computation Query
Ross Ihaka