Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with Ordered L1-Norm
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:780-789, 2016.
In this paper we propose a primal-dual proximal extragradient algorithm to solve the generalized Dantzig selector (GDS) estimation problem, based on a new convex-concave saddle-point (SP) reformulation. Our new formulation makes it possible to adopt recent developments in saddle-point optimization, to achieve the optimal O(1/k) rate of convergence. Compared to the optimal non-SP algorithms, ours do not require specification of sensitive parameters that affect algorithm performance or solution quality. We also provide a new analysis showing a possibility of local acceleration to achieve the rate of O(1/k^2) in special cases even without strong convexity or strong smoothness. As an application, we propose a GDS equipped with the ordered \ell_1-norm, showing its false discovery rate control properties in variable selection. Algorithm performance is compared between ours and other alternatives, including the linearized ADMM, Nesterov’s smoothing, Nemirovski’s mirror-prox, and the accelerated hybrid proximal extragradient techniques.