Performance of first- and second-order methods for $$\ell _1$$ ℓ 1 -regularized least squares problems

Kimon Fountoulakis () and Jacek Gondzio ()
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Kimon Fountoulakis: The University of Edinburgh
Jacek Gondzio: The University of Edinburgh

Computational Optimization and Applications, 2016, vol. 65, issue 3, No 4, 605-635

Abstract: Abstract We study the performance of first- and second-order optimization methods for $$\ell _1$$ ℓ 1 -regularized sparse least-squares problems as the conditioning of the problem changes 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: $$\ell _1$$ ℓ 1 -Regularised least-squares; First-order methods; Second-order methods; Sparse least squares instance generator; Ill-conditioned problems (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10589-016-9853-x

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