Fountoulakis, Kimon; Gondzio, J
We study the performance of first- and second-order optimization methods for ℓ1-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.
Słowa kluczowe: ℓ1-Regularised least-squaresFirst-order methodsSecond-order methodsSparse least squares instance generatorIll-conditioned problems