Hi Elena, Thanks for this intriguing idea. As far as I ever knew IRLS requires a matrix. Can you provide me with a citation where I can read about this vector-based approach?
Thanks, Eric On Thu, May 23, 2019, 06:44 Елена Картышева <el.kartysh...@yandex.ru> wrote: > Hello. > > I would like to propose a pull request implementing an option to use > variance vector instead of covariance matrix. It allows users to avoid > unnecessary memory usage and excessive computation in case of uncorrelated > but heteroscedastic errors thus making it possible to work with huge input > matrices. Using variance vector in such cases allows to reduce time > complexity from O(N^2) to just O(N) (where N is a number of observations) > and dramatically reduce memory usage. For example, in my practice arose a > need to train generalized linear model. Usage of Iteratively reweighted > least squares algorithm requires weighted regression with more than a > million observations. Current implementation would require approximately 12 > terabytes of memory while patched version needs only 8 megabytes. Since > IRLS is iterative algorithm a million-times complexity reduction is also > pretty handy. > > > -- > Sincerely yours, Elena Kartysheva. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > >
