10 citations · 30 across the 9 of their papers we have counts for
9 papers
Rethinking Remaining Useful Life Prediction with Scarce Time Series Data: Regression under Indirect Supervision
Jiaxiang Cheng, Yipeng Pang, Guoqiang Hu
Supervised time series prediction relies on directly measured target variables, but real-world use cases such as predicting remaining useful life (RUL) involve indirect supervision…
A Gradient-Free Distributed Optimization Method for Convex Sum of Non-Convex Cost Functions
Yipeng Pang, Guoqiang Hu
This paper presents a special type of distributed optimization problems, where the summation of agents' local cost functions (i.e., global cost function) is convex, but each indivi…
Social Profit Optimization with Demand Response Management in Electricity Market: A Multi-timescale Leader-following Approach
Jianzheng Wang, Yipeng Pang, Guoqiang Hu
In the electricity market, it is quite common that the market participants make "selfish" strategies to harvest the maximum profits for themselves, which may cause the social benef…
Distributed Nash Equilibrium Seeking in N-Cluster Games with Fully Uncoordinated Constant Step-Sizes
Yipeng Pang, Guoqiang Hu
Distributed optimization and Nash equilibrium (NE) seeking problems have drawn much attention in the control community recently. This paper studies a class of non-cooperative games…
Gradient-Free Nash Equilibrium Seeking in N-Cluster Games with Uncoordinated Constant Step-Sizes
Yipeng Pang, Guoqiang Hu
This work investigates a problem of simultaneous global cost minimization and Nash equilibrium seeking, which commonly exists in -cluster non-cooperative games. Specifically, th…
Nash Equilibrium Seeking in N-Coalition Games via a Gradient-Free Method
Yipeng Pang, Guoqiang Hu
This paper studies an -coalition non-cooperative game problem, where the players in the same coalition cooperatively minimize the sum of their local cost functions under a direc…
Gradient-Free Distributed Optimization with Exact Convergence
Yipeng Pang, Guoqiang Hu
In this paper, a gradient-free distributed algorithm is introduced to solve a set constrained optimization problem under a directed communication network. Specifically, at each tim…
Distributed Nash Equilibrium Seeking with Limited Cost Function Knowledge via A Consensus-Based Gradient-Free Method
Yipeng Pang, Guoqiang Hu
This paper considers a distributed Nash equilibrium seeking problem, where the players only have partial access to other players' actions, such as their neighbors' actions. Thus, t…
Randomized Gradient-Free Distributed Online Optimization via a Dynamic Regret Analysis
Yipeng Pang, Guoqiang Hu
This work considered an online distributed optimization problem, with a group of agents whose local objective functions vary with time. Moreover, the value of the objective functio…