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Re: Symmetric Matrices: time and space efficient represenation

From: Michele Martone
Subject: Re: Symmetric Matrices: time and space efficient represenation
Date: Fri, 19 Feb 2016 16:49:43 +0100
User-agent: Mutt/1.5.24 (2015-08-30)

On address@hidden:30, JuanPi wrote:
> ...
> The area of application is all kernel regression methods (e.g.
> Guassian processes, Gaussian Bayesian estimation)
> The covariance matrices are symmetric and most of the time not sparse.
> > If you deal only with dense symmetric, the standard approach is
> > ATLAS/LAPACK & co.
> I will se what they have, but I did not see anything exploiting
> symmetry directly.
> Thanks

Assuming you want basic operations then the BLAS will suffice.
In the BLAS (and LAPACK as well, for that matters) symmetry is 
in the routine name; see the routines containing xSY in their names 
in here:
Same story for LAPACK:

 subroutine     dsysv (UPLO, N, NRHS, A, LDA, IPIV, B, LDB, WORK, LWORK, INFO)
 DSYSV computes the solution to system of linear equations A * X = B for SY 
matrices More...

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