期刊
SIAM JOURNAL ON OPTIMIZATION
卷 23, 期 2, 页码 1092-1125出版社
SIAM PUBLICATIONS
DOI: 10.1137/110833786
关键词
complexity; extragradient; variational inequality; maximal monotone operator; proximal point; ergodic convergence; hybrid; convex programming; accelerated gradient; accelerated Newton
资金
- NSF [CCF-0808863, CMMI-0900094, CMMI-1300221]
- ONR [ONR N00014-11-1-0062]
- CNPq [303583/2008-8, 302962/2011-5, 480101/2008-6, 474944/2010-7]
- FAPERJ [E-26/102.821/2008, E-26/102.940/2011]
- PRONEX-Optimization
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1300221] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [0900094] Funding Source: National Science Foundation
This paper presents an accelerated variant of the hybrid proximal extragradient (HPE) method for convex optimization, referred to as the accelerated HPE (A-HPE) framework. Iteration-complexity results are established for the A-HPE framework, as well as a special version of it, where a large stepsize condition is imposed. Two specific implementations of the A-HPE framework are described in the context of a structured convex optimization problem whose objective function consists of the sum of a smooth convex function and an extended real-valued nonsmooth convex function. In the first implementation, a generalization of a variant of Nesterov's method is obtained for the case where the smooth component of the objective function has Lipschitz continuous gradient. In the second implementation, an accelerated Newton proximal extragradient (A-NPE) method is obtained for the case where the smooth component of the objective function has Lipschitz continuous Hessian. It is shown that the A-NPE method has a O(1/k(7/2)) convergence rate, which improves upon the O(1/k(3)) convergence rate bound for another accelerated Newton-type method presented by Nesterov. Finally, while Nesterov's method is based on exact solutions of subproblems with cubic regularization terms, the A-NPE method is based on inexact solutions of subproblems with quadratic regularization terms and hence is potentially more tractable from a computational point of view.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据