5 papers
Adaptive Polyak Stepsize with Level-value Adjustment for Distributed Optimization
Chen Ouyang, Yongyang Xiong, Jinming Xu +2
Stepsize selection remains a critical challenge in the practical implementation of distributed optimization. Existing distributed algorithms often rely on restrictive prior knowled…
Loopless Proximal Riemannian Gradient EXTRA for Distributed Optimization on Compact Manifolds
Yongyang Xiong, Chen Ouyang, Keyou You +2
Distributed optimization has gained substantial interest in recent years due to its wide applications in machine learning. However, most of existing algorithms are designed for Euc…
Heterogeneous Stochastic Momentum ADMM for Distributed Nonconvex Composite Optimization
Yangming Zhang, Yongyang Xiong, Jinming Xu +2
This paper investigates the distributed stochastic nonconvex and nonsmooth composite optimization problem. Existing stochastic typically rely on uniform step size strictly bounded…
Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
Pu Qiu, Chen Ouyang, Yongyang Xiong +3
Federated Composite Optimization (FCO) has emerged as a promising framework for training models with structural constraints (e.g., sparsity) in distributed edge networks. However,…
A Unified Hybrid Control Architecture for Multi-DOF Robotic Manipulators
Xinyu Qiao, Yongyang Xiong, Yu Han +1
Multi-degree-of-freedom (DOF) robotic manipulators exhibit strongly nonlinear, high-dimensional, and coupled dynamics, posing significant challenges for controller design. To addre…