Abstract

Increasingly, optimization problems in machine learning, especially those arising from bigh-dimensional statistical estimation, bave a large number of variables. Modem statistical estimators developed over the past decade have statistical or sample complexity that depends only weakly on the number of parameters when there is some structore to the problem, such as sparsity. A central question is whether similar advances can be made in their computational complexity as well. In this paper, we propose strategies that indicate that such advances can indeed be made. In particular, we investigate the greedy coordinate descent algorithm, and note that performing the greedy step efficiently weakens the costly dependence on the problem size provided the solution is sparse. We then propose a snite of methods that perform these greedy steps efficiently by a reduction to nearest neighbor search. We also devise a more amenable form of greedy descent for composite non-smooth objectives; as well as several approximate variants of such greedy descent. We develop a practical implementation of our algorithm that combines greedy coordinate descent with locality sensitive hashing. Without tuning the latter data structore, we are not only able to significantly speed up the vanilla greedy method, hot also outperform cyclic descent when the problem size becomes large. Our resnlts indicate the effectiveness of our nearest neighbor strategies, and also point to many open questions regarding the development of computational geometric techniques tailored towards first-order optimization methods.