[SciPy-dev] code for incremental least squares
Wed Feb 17 02:32:45 CST 2010
2010/2/16 Stéfan van der Walt <firstname.lastname@example.org>:
> Forming the product A^T A is often a bad idea from a numerical
> In Chapter 3 of Ake Bjork's "Numerical Methods for Least
> Squares Problems", he talks about "update" problems. He mentions that
> "...the solution should be accurate up to the limitation of data and
> conditioning of the problem; i.e., a stable numerical method must be
> He describes the Kalman-based update method (which bears some
> resemblance to yours), but says that "the main disadvantage...is its
> serious sensitivity to roundoff errors. The updating algorithms based
> on orthogonal transformations developed in the following sections are
> generally to be preferred." He then goes into more detail on updating
> the QR and Gram-Schmidt decompositions.
> Not sure if that helps, but it may be worth reading that chapter.
Definitely -- thanks for the reference! Looks like the library has a
copy available, too...
(Not that I'll necessarily bother for my own use, since like I said,
my problems seem to be fairly well conditioned and the sparse X'X +
Cholesky approach is very fast, and also generalizes easily to mixed
effect models, which I'm currently working on implementing.)
>> For the QR code I use a recurrence relation I stole from some slides
>> by Simon Wood to compute R and Q'y incrementally; probably a real
>> incremental QR (e.g., "AS274", which is what R's biglm package uses)
>> would be better, but this one is easy to implement in terms of
>> non-incremental QR.
> Incremental QR is something we should implement in scipy.linalg, for sure.
More information about the SciPy-Dev