Re: [eigen] RFC: BasicSVD class |

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*To*: eigen@xxxxxxxxxxxxxxxxxxx*Subject*: Re: [eigen] RFC: BasicSVD class*From*: Mark Borgerding <mark@xxxxxxxxxxxxxx>*Date*: Mon, 10 Dec 2012 10:07:16 -0500

Thanks for your assessment of "not useless" ;). To specify your
statement below: the algorithm struggles with getting the
smallest singular values from ill-conditioned matrices. This
is the harder problem, to be sure, than finding the dominant
dimensions. It is impressive that JacobiSVD can do such a great job
with pulling out dimensions that are 10 orders of magnitude down. However ... Getting the weakest dimensions is not what I need an
SVD for. I want the first few dominant dimensions. I suspect this
is a common requirement (hence matlab eigs and svds functions). I
have been frustated in the past by attempts to use JacobiSVD because
of the hundred-fold speed difference compared to the LAPACK
functions ( sgesdd, cgesdd, dgesdd, zgesdd )I'm not suggesting BasicSVD replace JacobiSVD. I'm offering it as an alternative. On the subject of power iteration. Note that what I implemented is not textbook power iteration. I use feedback guided by the maximization of the Rayleigh Quotient. If you want to disable the feedback, comment out the lines with "damp" in them. On 12/10/2012 09:12 AM, Jitse Niesen wrote: On Fri, 7 Dec 2012, Gael Guennebaud wrote: |

**Follow-Ups**:**Re: [eigen] RFC: BasicSVD class***From:*Benoit Jacob

**References**:**[eigen] RFC: BasicSVD class***From:*Mark Borgerding

**Re: [eigen] RFC: BasicSVD class***From:*Mark Borgerding

**Re: [eigen] RFC: BasicSVD class***From:*Gael Guennebaud

**Re: [eigen] RFC: BasicSVD class***From:*Jitse Niesen

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