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Asked: 2026-06-13T12:59:08+00:00

I’m looking for a fast svd library, in either c, c++ or java. Ultimately

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I’m looking for a fast svd library, in either c, c++ or java. Ultimately I’m using Java, but I’m very comfortable using jna to wrap c++, eg http://github.com/hughperkins/jeigen

I’m looking for a fast svd library that will handle sparse matrices. To keep this objective, so that the question doesn’t get marked as too subjective, let’s say:

I looked around at a few libraries and found:

  • matlab: super fast, about 10 seconds, but it’s not really a ‘library’ as such. average squared projection error: 0.93
  • redsvd: super fast, about 1 second to run, for 6 features, but the average squared projection error is 0.97, which is very high
  • Eigen’s svd is both very slow, and only for dense matrices
  • svdlibc: ran for 28 minutes before I stopped it; I guess it’s calculating the full S, rather than just the first 6 features or so

Basically, I’m looking for a library that gives about the same speed and average squared projection error as matlab, or at least, somewhat comparable.

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1 Answer

  1. Editorial Team

    From my experience, svdlibc is the best library of those options. I’ve dug a bit through its code before and I don’t believe it’s calculating the full S matrix (i.e., it is a true “thin svd”). If you can control the matrix representation on disk, svdlibc performs much faster when using the sparse binary input format due to the significantly lower I/O overhead.

    The S-Space Package provided an executable jar around the SVDLIBJ java port of SVDLIBC. However, they found it had different results than SVDLIBC for certain input solutions.

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