Hello, I've been successfully using the Python NLopt interface to minimize my (convex) functions of a rather high number of variables (100-1000) using L-BFGS, however, when the dimensionality of the problem increases, the results are highly contaminated by noise.
I thought I'd try to solve this problem by adding a penalty term that consists of an L1-norm of the variables, because I'm specifically looking for sparse solutions, but it doesn't seem to work: the optimization never converges. Out of curiosity, I've also tried an L2-norm, and this worked, so I suspect that I'm having this problem because L1-norm makes the function not differentiable at zero. Is there any built-in facility in NLopt for L1-norm regularization that I've missed? Have anyone got L1-norm regularization to work with NLopt gradient-based methods before? Thanks, -- Sincerely yours, Yury V. Zaytsev _______________________________________________ NLopt-discuss mailing list [email protected] http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss
