2 citations · 4 across the 6 of their papers we have counts for
6 papers
Fast Gradient Algorithm with Dry-like Friction and Nonmonotone Line Search for Nonconvex Optimization Problems
Lien T. Nguyen, Andrew Eberhard, Xinghuo Yu +1
In this paper, we propose a fast gradient algorithm for the problem of minimizing a differentiable (possibly nonconvex) function in Hilbert spaces. We first extend the dry friction…
Reinforcement Learning-based Home Energy Management with Heterogeneous Batteries and Stochastic EV Behaviour
Meng Yuan, Ye Wang, Xinghuo Yu +2
The widespread adoption of photovoltaic (PV), electric vehicles (EVs), and stationary energy storage systems (ESS) in households increases system complexity while simultaneously of…
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…
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…
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…
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network
Mohammadreza Radmanesh, Hossein Ghorbanzadeh, Ahmad Asgharian Rezaei +2
Recently network embedding has gained increasing attention due to its advantages in facilitating network computation tasks such as link prediction, node classification and node clu…