Performance of first- and second-order methods for $$\ell _1$$ l 1 -regularized least squares problems
研究了当问题条件数变化且维度增至一万亿时，一阶和二阶优化方法在ℓ1正则化稀疏最小二乘问题中的表现，并提出了一个低内存、可扩展的问题生成器。
We study the performance of first- and second-order optimization methods for $$\ell _1$$ -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.