  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.
