hpcc-systems/ecl-ml — pull request #185 by emuharemagic (2014-03-04 22:31:47+00:00)
Title: ML-224 SVD decomposition does not produce original matrix

https://track.hpccsystems.com/i#browse/ML-224
https://track.hpccsystems.com/i#browse/ML-226

- Remove Lanczos optimization from calculation of eigen vectors and values

There's a number of optimizations that can be made in the ecl-ml matrix
library whose implementation is based on sparse matrix representation.
For example, in the 'strict' mode of operation, matrix dimensions should always
be explicitly set, so that matrix dimension requirements can be enforced.
Once this is done, it should be possible to perform matrix thinning and
value rounding to prevent unnecessarily carrying very small real numbers
through iterations, but without possibility of losing matrix dimensions during
that process. This in turn will prevent error propagation, and it will make it possible
to re-introduce other matrix operations optimizations, such as using Lanczos
algorithm in calculation of eigen vectors.

Signed-off-by: Edin Muharemagic