22 citations · 56 across the 13 of their papers we have counts for
15 papers
Local adapt-then-combine algorithms for distributed nonsmooth optimization: Achieving provable communication acceleration
Luyao Guo, Xinli Shi, Wenying Xu +1
This paper is concerned with the distributed composite optimization problem over networks, where agents aim to minimize a sum of local smooth components and a common nonsmooth term…
Distributed Online Randomized Gradient-Free Optimization with Compressed Communication
Longkang Zhu, Xinli Shi, Xiangping Xu +2
This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose a unified fr…
Distributed Online Randomized Gradient-Free optimization with Compressed Communication
Longkang Zhu, Xinli Shi, Xiangping Xu +1
This paper addresses two fundamental challenges in distributed online convex optimization: communication efficiency and optimization under limited feedback. We propose Online Compr…
Perturbed Proximal Gradient ADMM for Nonconvex Composite Optimization
Yuan Zhou, Xinli Shi, Luyao Guo +2
This paper proposes a Perturbed Proximal Gradient ADMM (PPG-ADMM) framework for solving general nonconvex composite optimization problems, where the objective function consists of…
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Yuan Zhou, Xinli Shi, Xuelong Li +3
Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server. When local objectives are nonconvex and coupled with nonsmooth weak…
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
Yuan Zhou, Jiachen Zhong, Xinli Shi +2
Composite federated learning offers a general framework for solving machine learning problems with additional regularization terms. However, existing methods often face significant…
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
Luyao Guo, Luqing Wang, Xinli Shi +1
Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform sever…
Fixed-Time Gradient Flows for Solving Constrained Optimization: A Unified Approach
Xinli Shi, Xiangping Xu, Guanghui Wen +1
The accelerated method in solving optimization problems has always been an absorbing topic. Based on the fixed-time (FxT) stability of nonlinear dynamical systems, we provide a uni…
Extended Zero-Gradient-Sum Approach for Constrained Distributed Optimization with Free Initialization
Xinli Shi, Xinghuo Yu, Guanghui Wen +1
This paper proposes an extended zero-gradient-sum (EZGS) approach for solving constrained distributed optimization (DO) with free initialization. A Newton-based continuous-time alg…
A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack
Zhuoxuan Li, Iakov Korovin, Xinli Shi +4
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and…
Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization
Luyao Guo, Xinli Shi, Jinde Cao +1
This paper proposes a novel CTA (Combine-Then-Adapt)-based decentralized algorithm for solving convex composite optimization problems over undirected and connected networks. The lo…
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization
Luyao Guo, Jinde Cao, Xinli Shi +1
In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss f…
Differentially Private Decentralized Optimization with Relay Communication
Luqing Wang, Luyao Guo, Shaofu Yang +1
Security concerns in large-scale networked environments are becoming increasingly critical. To further improve the algorithm security from the design perspective of decentralized o…
Finite-Time Convergent Algorithms for Time-Varying Distributed Optimization
Xinli Shi, Guanghui Wen, Xinghuo Yu
This paper focuses on finite-time (FT) convergent distributed algorithms for solving time-varying (TV) distributed optimization (TVDO). The objective is to minimize the sum of loca…
DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization
Luyao Guo, Xinli Shi, Shaofu Yang +1
In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth…