Jingwang Li
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Research Assistant |
About
Currently, I am a research assistant at The Chinese University of Hong Kong, Shenzhen, working on decentralized optimization under the supervision of Prof. Xiao Li. Previously, I was an algorithm engineer at FABU.AI, engaging in modeling and solving optimization problems related to autonomous driving. Before that, I received a BSc in Engineering Management from Huazhong Agricultural University and an MPhil in Control Science & Engineering under the supervision of Prof. Housheng Su from Huazhong University of Science & Technology.
News
Dec. 2023: Our work “NPGA: A unified algorithmic framework for decentralized constraint-coupled optimization” has been accepted by IEEE Transactions on Control of Network Systems.
Sept. 2023: I have been an RA at CUHKSZ.
Sept. 2023: I resigned from FABU.AI.
Research Interests
My research interests primarily revolve around the theory and applications of decentralized optimization. Currently, I am particularly interested in developing communication and computation efficient algorithms to solve a class of decentralized optimization problems with globally coupled constraints. In this context, we usually consider a networked system consisting of \(n\) agents, and the underlying network topology can be represented by an undirected or directed graph. The problem of interest can be formulated as follows:
\[ \begin{equation} \begin{aligned} \min_{x_i \in \mathbb{R}^{d_i}, y \in \mathbb{R}^p} \ & \sum_{i=1}^n \left(f_i(x_i) + g_i(x_i)\right) + h(y) \\ \text{s.t.} \ & \sum_{i=1}^{n}A_ix_i = y, \end{aligned} \end{equation} \label{original_pro} \]
where \(f_i:\mathbb{R}^{d_i} \rightarrow \mathbb{R}\), \(g_i:\mathbb{R}^{d_i} \rightarrow \mathbb{R} \cup \{+\infty\}\) and \(A_i \in \mathbb{R}^{p \times d_i}\) are private for agent \(i\), while \(h:\mathbb{R}^p \rightarrow \mathbb{R} \cup \{+\infty\}\) is public for all agents. The problem \(\eqref{original_pro}\) captures a wide range of real-world scientific and engineering problems, including decentralized vertical federated learning, decentralized optimal transport, decentralized resource allocation, and more, which is exactly why it appeals to me so much.
Publications
* indicates the corresponding author.
Preprints
Jingwang Li and Housheng Su*. “Decentralized constraint-coupled optimization with inexact oracle”, arXiv:2309.06330, 2023. [arXiv]
Jingwang Li and Housheng Su*. “Gradient tracking: A unified approach to smooth distributed optimization”, arXiv:2202.09804, 2022. [arXiv]
Journal Articles
Jingwang Li and Housheng Su*. “NPGA: A unified algorithmic framework for decentralized constraint-coupled optimization”, IEEE Transactions on Control of Network Systems, 2024. [DOI] [arXiv]
Jingwang Li, Qing An and Housheng Su*. “Proximal nested primal-dual gradient algorithms for distributed constraint-coupled composite optimization”, Applied Mathematics and Computation, 2023. [DOI]
Jingwang Li and Housheng Su*. “Implicit tracking-based distributed constraint-coupled optimization”, IEEE Transactions on Control of Network Systems, 2022. [DOI] [arXiv]
Conference Proceedings
Bingxi Jia*, Haidong Ren, Zheng Yang, Jingwang Li and Xiaofei He. “Cascaded motion estimation for intelligent vehicles in GNSS-denied scenes”, Chinese Control Conference, 2023. [DOI]
Jingwang Li, Suoxia Miao and Housheng Su*. “Distributed primal-dual mirror dynamics for constraint-coupled optimization”, International Conference on Guidance, Navigation and Control, 2022. [DOI]
Academic Services
Reviewer of
IEEE Transactions on Control of Network Systems
IEEE Transactions on Systems, Man and Cybernetics: Systems
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