Luc, Last I looked, I think I saw that commons math used a double indirect storage format very similar to Jama.
Is there any thought to going to a higher performance layout such as used by Colt? On Thu, Nov 27, 2008 at 9:10 AM, <[EMAIL PROTECTED]> wrote: > Hello, > > This commit is the result of weeks of work. I hope it completes an > important feature > to [math], computation of eigenvalues and eigenvectors for symmetric real > matrices. > > The implementation is based on algorithms developed in the last 10 years or > so. It is based partly on two reference papers and partly on LAPACK. Lapack > is distributed under a modified-BSD license, so this is acceptable for > [math]. I have updated the NOTICE file and taken care of the proper > attributions in Javadoc. > > The current status is that we can solve eigenproblems much faster than Jama > (see the speed gains in the commit message below). Furthermore, the > eigenvectors are not always computed, they are computed only if needed. So > applications that only need eigenvalues will benefit from a larger speed > gain. This could even be improved again by allowing to compute only some > eigenvalues, not all of them. This feature is available in the higher level > LAPACK routine, but I didn't include it yet. I'll do it only when required, > as this as already been a very large amount of work. > > If someone could test this new decomposition algorithm further, I would be > more than happy. > > My next goal is now to implement Singular Value Decomposition. I will most > probably use a method based on eigen decomposition as this seems to be now > the prefered way since dqd/dqds and MRRR algorithms are available. > > Luc > > ----- [EMAIL PROTECTED] a écrit : > > > Author: luc > > Date: Thu Nov 27 07:50:42 2008 > > New Revision: 721203 > > > > URL: http://svn.apache.org/viewvc?rev=721203&view=rev > > Log: > > completed implementation of EigenDecompositionImpl. > > The implementation is now based on the very fast and accurate dqd/dqds > > algorithm. > > It is faster than Jama for all dimensions and speed gain increases > > with dimensions. > > The gain is about 30% below dimension 100, about 50% around dimension > > 250 and about > > 65% for dimensions around 700. > > It is also possible to compute only eigenvalues (and hence saving > > computation of > > eigenvectors, thus even increasing the speed gain). > > JIRA: MATH-220 > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [EMAIL PROTECTED] > For additional commands, e-mail: [EMAIL PROTECTED] > > -- Ted Dunning, CTO DeepDyve 4600 Bohannon Drive, Suite 220 Menlo Park, CA 94025 www.deepdyve.com 650-324-0110, ext. 738 858-414-0013 (m)
