5 papers
Revisiting Randomized Smoothing: Nonsmooth Nonconvex Optimization Beyond Global Lipschitz Continuity
Jingfan Xia, Zhenwei Lin, Qi Deng
Randomized smoothing is a widely adopted technique for optimizing nonsmooth objective functions. However, its efficiency analysis typically relies on global Lipschitz continuity, a…
Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization
Qi Deng, Guanghui Lan, Zhenwei Lin
This paper presents new first-order methods for achieving optimal oracle complexities in convex optimization with convex functional constraints. Oracle complexities are measured by…
A Single-Loop Robust Policy Gradient Method for Robust Markov Decision Processes
Zhenwei Lin, Chenyu Xue, Qi Deng +1
Robust Markov Decision Processes (RMDPs) have recently been recognized as a valuable and promising approach to discovering a policy with creditable performance, particularly in the…
Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
Zhenwei Lin, Jingfan Xia, Qi Deng +1
We consider decentralized gradient-free optimization of minimizing Lipschitz continuous functions that satisfy neither smoothness nor convexity assumption. We propose two novel gra…
Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints
Zhenwei Lin, Qi Deng
In this paper, we introduce faster accelerated primal-dual algorithms for minimizing a convex function subject to strongly convex function constraints. Prior to our work, the best…