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https://issues.apache.org/jira/browse/MATH-355?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13068473#comment-13068473
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Christopher Nix commented on MATH-355:
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I have a partial implementation that is still missing some pre-processing to
improve performance. Currently, it fails to beat the JAMA code, hence my patch
on MATH-611. I don't believe that the performance of my code can be improved
to beat JAMA, though I am willing to continue development slow time.
If I understand correctly, the algorithm in question is not necessarily the
best SVD algorithm available, it merely pushes the boundaries of what Jacobi
methods for SVD are capable of achieving compared to, say, bi- or
tri-diagonalisation methods.
I would certainly not regard this as 'Major' issue anymore. I could pursue
development further if it is considered useful to have another implementation
within ACM (raising a new issue later if required to submit).
Chris.
> State of the art SVD Algorithm
> ------------------------------
>
> Key: MATH-355
> URL: https://issues.apache.org/jira/browse/MATH-355
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 2.0, 2.1
> Reporter: Bruce A Johnson
> Labels: svd
> Fix For: 3.0
>
>
> There has been a lot of recent activity on the SVD algorithm for Commons
> Math. The SVD has also been in the news lately because of the development of
> a new algorithm that is both faster and more accurate than previous
> algorithms. Given the importance of the SVD in many applications it would be
> useful to have a Java implementation of this new algorithm in Commons Math.
> News article on the new algorithm:
> http://www.siam.org/pdf/news/1696.pdf
> Manuscripts on the new algorithm:
> http://www.netlib.org/lapack/lawnspdf/lawn169.pdf
> http://www.netlib.org/lapack/lawnspdf/lawn170.pdf
> Implementation in LAPACK 3.2.1
> dgesvj.f
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