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Performance of First- and Second-Order Methods for L1-Regularized Least Squares Problems

Kimon Fountoulakis (kfount***at***berkeley.edu)
Jacek Gondzio (J.Gondzio***at***ed.ac.uk)

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.

Keywords: l1-regularised least-squares, First-order methods, Second-order methods, Sparse least squares instance generator, Ill-conditioned problems

Category 1: Convex and Nonsmooth Optimization

Citation: Technical Report ERGO 15-005

Download: [PDF]

Entry Submitted: 03/11/2015
Entry Accepted: 03/11/2015
Entry Last Modified: 12/14/2015

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