From the 2 of 8 linked papers with an AI index.
8 papers
Scalable Dynamic Optimal Transport via Distributed Linearized ADMM
Hari Dahal, Rongjie Lai, Yangyang Xu
The paper proposes a reformulation of the dynamic optimal transport problem that admits an exact proximal mapping and combines it with a linearized ADMM algorithm to remain stable…
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…
LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication
Wei Liu, Anweshit Panda, Ujwal Pandey +5
In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adaptive gradient methods, such a…
Inexact Moreau Envelope Lagrangian Method for Non-Convex Constrained Optimization under Local Error Bound Conditions on Constraint Functions
Yankun Huang, Qihang Lin, Yangyang Xu
In this paper, we investigate how structural properties of the constraint system impact the oracle complexity of smooth non-convex optimization problems with convex inequality cons…
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…