From: Ian Scott <scottim@im...>  20110922 18:03:03

Chris, Pretty much all of vnl's interesting algorithms are wrappers around bits of netlib (LAPACK, LINPACK, MINPACK, etc.) Where LAPACK provides a good algorithm for something, vnl will not have anything better. The main advantage of VNL over BLAS and LAPACK is a better API. That was just a general comment, there may be some specific algorithm in VNL that solves your problem optimally fast. Although if I understand linear algebra well enough, only Gauss elimination is faster than LU decomposition in the dense, nonsingular, but otherwise unconstrained case. Anyway, I can't find an implementation of Gauss elimination or LU decomposition in VNL. As to faster options, feel free to add a wrapper for LAPACKs LU decomposition function. Sorry I can't be more help. Ian. On 22/09/2011 17:34, baumgach@... wrote: > Hi all, > > I changed an existing implementation for solving a linear system using > LAPACK to VNL. The LAPACK implementation used LU decomposition for > solving the system. The fastest equivalent for dense matrices I could > find for VNL was vnl_qr, however this is too slow for my application. > So my question is: is vnl_qr the fastest way to solve the linear > system AX=B where A,X,B are dense matrices, (and A is symmetric and > pos. def.) or is there a better way? > > Thanks a lot for your help! > > > >  > All the data continuously generated in your IT infrastructure contains a > definitive record of customers, application performance, security > threats, fraudulent activity and more. Splunk takes this data and makes > sense of it. Business sense. IT sense. Common sense. > http://p.sf.net/sfu/splunkd2dcopy1 > _______________________________________________ > Vxlusers mailing list > Vxlusers@... > https://lists.sourceforge.net/lists/listinfo/vxlusers > 