commons-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Luc Maisonobe (JIRA)" <>
Subject [jira] Commented: (MATH-321) Support for Sparse (Thin) SVD
Date Sun, 06 Dec 2009 19:06:18 GMT


Luc Maisonobe commented on MATH-321:

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:
>             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

This message is automatically generated by JIRA.
You can reply to this email to add a comment to the issue online.

View raw message