activity
20182022
most citedA Distributed GNE Seeking Algorithm Using the Douglas-Rachford Splitting Method

1 citations · 1 across the 4 of their papers we have counts for

collaborators

8 papers

math.OC2022

Distributed Stochastic Nash Equilibrium Learning in Locally Coupled Network Games with Unknown Parameters

Yuanhanqing Huang, Jianghai Hu

In stochastic Nash equilibrium problems (SNEPs), it is natural for players to be uncertain about their complex environments and have multi-dimensional unknown parameters in their m…

eess.SY2021

A Primal Decomposition Approach to Globally Coupled Aggregative Optimization over Networks

Yuanhanqing Huang, Jianghai Hu

We consider a class of multi-agent optimization problems, where each agent has a local objective function that depends on its own decision variables and the aggregate of others, an…

math.OC20211 cited

A Distributed GNE Seeking Algorithm Using the Douglas-Rachford Splitting Method

Yuanhanqing Huang, Jianghai Hu

We consider a generalized Nash equilibrium problem (GNEP) for a network of players. Each player tries to minimize a local objective function subject to some resource constraints wh…

cs.AI2021

Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction

Donghwan Lee, Niao He, Seungjae Lee +2

The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings. Inspire…

math.OC2020

Column Partition based Distributed Algorithms for Coupled Convex Sparse Optimization: Dual and Exact Regularization Approaches

Jinglai Shen, Jianghai Hu, Eswar Kumar Hathibelagal Kammara

This paper develops column partition based distributed schemes for a class of large-scale convex sparse optimization problems, e.g., basis pursuit (BP), LASSO, basis pursuit denosi…

math.OC2018

Supplemental Material For "Primal-Dual Q-Learning Framework for LQR Design"

Donghwan Lee, Jianghai Hu

Recently, reinforcement learning (RL) is receiving more and more attentions due to its successful demonstrations outperforming human performance in certain challenging tasks. In ou…