From the 1 of 6 linked papers with an AI index.
6 papers
A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization
Wei Liu, Muhammad Khan, Gabriel Mancino-Ball +1
The paper introduces a stochastic smoothing proximal gradient algorithm for solving nonconvex‑nonconcave minimization‑expectation‑maximization (minEmax) problems, providing converg…
Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints
Hari Dahal, Wei Liu, Yangyang Xu
We give a damped proximal augmented Lagrangian method (DPALM) for solving problems with a weakly-convex objective and convex linear/nonlinear constraints. Instead of taking a full…
A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization
Wei Liu, Yangyang Xu
Many real-world problems, such as those with fairness constraints, involve complex expectation constraints and large datasets, necessitating the design of efficient stochastic meth…
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
Wei Liu, Anweshit Panda, Ujwal Pandey +6
In this paper, we design two compressed decentralized algorithms for solving nonconvex stochastic optimization under two different scenarios. Both algorithms adopt a momentum techn…
Lower Complexity Bounds of First-order Methods for Affinely Constrained Composite Non-convex Problems
Wei Liu, Qihang Lin, Yangyang Xu
Many recent studies on first-order methods (FOMs) focus on \emph{composite non-convex non-smooth} optimization with linear and/or nonlinear function constraints. Upper (or worst-ca…
A Near-optimal Method for Linearly Constrained Composite Non-convex Non-smooth Problems
Wei Liu, Qihang Lin, Yangyang Xu
We study first-order methods (FOMs) for solving \emph{composite nonconvex nonsmooth} optimization with linear constraints. Recently, the lower complexity bounds of FOMs on finding…