26 citations · 38 across the 7 of their papers we have counts for
15 papers · 1 filter
Mirror-prox sliding methods for solving a class of monotone variational inequalities
Guanghui Lan, Yuyuan Ouyang
In this paper we propose new algorithms for solving a class of structured monotone variational inequality (VI) problems over compact feasible sets. By identifying the gradient comp…
Faster Algorithm and Sharper Analysis for Constrained Markov Decision Process
Tianjiao Li, Ziwei Guan, Shaofeng Zou +3
The problem of constrained Markov decision process (CMDP) is investigated, where an agent aims to maximize the expected accumulated discounted reward subject to multiple constraint…
Graph topology invariant gradient and sampling complexity for decentralized and stochastic optimization
Guanghui Lan, Yuyuan Ouyang, Yi Zhou
One fundamental problem in decentralized multi-agent optimization is the trade-off between gradient/sampling complexity and communication complexity. We propose new algorithms whos…
Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning
Georgios Kotsalis, Guanghui Lan, Tianjiao Li
The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evalu…
A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan +1
Nonconvex sparse models have received significant attention in high-dimensional machine learning. In this paper, we study a new model consisting of a general convex or nonconvex ob…
Convex optimization for finite horizon robust covariance control of linear stochastic systems
Georgios Kotsalis, Guanghui Lan, Arkadi Nemirovski
This work addresses the finite-horizon robust covariance control problem for discrete-time, partially observable, linear system affected by random zero mean noise and deterministic…