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

Published: 2015/03/11, Updated: 2015/12/14

Convex and Nonsmooth Optimization first-order methods, ill-conditioned problems, l1-regularised least-squares, second-order methods, sparse least squares instance generator Short URL: https://optimization-online.org/?p=13319

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.

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Technical Report ERGO 15-005

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