Good news: as of version 1.4, SVDLIBC is explicitly available under a BSD license.
SVDLIBC is a fast implementation of SVD matrix decomposition by Doug Rohde. It works particularly efficiently in the following cases:
- the matrix is sparse,
- only a few singular values are needed.
These properties make it particularly well suited for latent semantic analysis, for example.
I ran an experiment on an Amazon EC2 m2.xlarge instance - which might be
way overkill - with a 70k × 500k matrix containing 8M entries (density: 0.02%).
Here is the running time for different values of d (= dimensions = number of
singular values):
-d 50: 39s wall time-d 300: 4m53s wall time-d 1000: 31m48s wall time
The latest official release of the library (version 1.34) dates back from 2005. It has a few quirks, such as:
make/make installdon't work "as expected"- it doesn't compile on Mac OS X out of the box
- some bugs have been found, e.g. by piskvorky
I'm not a release engineer, and have only limited knowledge of the different
languages (C, Makefile) and tools (make, gcc) involved. The modifications in
this fork are working for me, but nothing guarantees they'll work for you.
If you find a bug and fix it yourself, I'd be happy to get a pull your changes over.
Easy as pie:
# Download the code. Alternatively you can also download the zip file.
git clone git://github.com/lucasmaystre/svdlibc.git
cd svdlibc
# Just like any other sane program...
make
make install
# You're done. Start using it!
svd -o result -d 10 THE_MATRIX