K. Fountoulakis and J. Gondzio
Abstract
We study the performance of first- and second-order optimization methods for
l1-regularized sparse least-squares problems as the conditioning and the
dimensions of the problem increase up to one trillion. A rigorously defined
generator is presented which allows control of the dimensions, the conditioning
and the sparsity of the problem. The generator has very low memory requirements
and scales well with the dimensions of the problem.
Key words: L1-regularized least-squares, First-order methods, Second-order methods, Sparse least-squares instance generator, Ill-conditioned problems.