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https://issues.apache.org/jira/browse/MATH-321?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12786673#action_12786673
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Luc Maisonobe commented on MATH-321:
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There is some work ongoing (very slowly though, I'm sorry about that) to
improve both SVD and eigen decomposition.
The current implementation is a simplified version of lapack DSTEMR. Part of
the simplification was to always compute all the eigenvalues, despite the
original lapack function allowed to select some of them, either by a value
range or by an index range. I want to remove this limitation and provide this
feature in commons-math too.
This would be a first step towards partial SVD.
Any help on implementing this is welcome.
> Support for Sparse (Thin) SVD
> -----------------------------
>
> Key: MATH-321
> URL: https://issues.apache.org/jira/browse/MATH-321
> Project: Commons Math
> Issue Type: New Feature
> Reporter: David Jurgens
>
> Current the SingularValueDecomposition implementation computes the full SVD.
> However, for some applications, e.g. LSA, vision applications, only the most
> significant singular values are needed. For these applications, the full
> decomposition is impractical, and for large matrices, computationally
> infeasible. The sparse SVD avoids computing the unnecessary data, and more
> importantly, has significantly lower computational complexity, which allows
> it to scale to larger matrices.
> Other linear algebra implementation have support for the sparse svd. Both
> Matlab and Octave have the svds function. C has SVDLIBC. SVDPACK is also
> available in Fortran and C. However, after extensive searching, I do not
> believe there is any existing Java-based sparse SVD implementation. This
> added functionality would be widely used for any pure Java application that
> requires a sparse SVD, as the only current solution is to call out to a
> library in another language.
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