10 citations · 16 across the 7 of their papers we have counts for
6 papers · 1 filter
A Novel Catalyst Scheme for Stochastic Minimax Optimization
Guanghui Lan, Yan Li
This paper presents a proximal-point-based catalyst scheme for simple first-order methods applied to convex minimization and convex-concave minimax problems. In particular, for smo…
First-order Policy Optimization for Robust Policy Evaluation
Yan Li, Guanghui Lan
We adopt a policy optimization viewpoint towards policy evaluation for robust Markov decision process with -rectangular ambiguity sets. The developed method, named firs…
Numerical Methods for Convex Multistage Stochastic Optimization
Guanghui Lan, Alexander Shapiro
Optimization problems involving sequential decisions in a stochastic environment were studied in Stochastic Programming (SP), Stochastic Optimal Control (SOC) and Markov Decision P…
Accelerated gradient sliding for structured convex optimization
Guanghui Lan, Yuyuan Ouyang
Our main goal in this paper is to show that one can skip gradient computations for gradient descent type methods applied to certain structured convex programming (CP) problems. To…
Fast Bundle-Level Type Methods for unconstrained and ball-constrained convex optimization
Yunmei Chen, Guanghui Lan, Yuyuan Ouyang +1
It has been shown in \cite{Lan13-1} that the accelerated prox-level (APL) method and its variant, the uniform smoothing level (USL) method, have optimal iteration complexity for so…
Accelerated Schemes For A Class of Variational Inequalities
Yunmei Chen, Guanghui Lan, Yuyuan Ouyang
We propose a novel method, namely the accelerated mirror-prox (AMP) method, for computing the weak solutions of a class of deterministic and stochastic monotone variational inequal…