Workshop 16

Organizers: Shandong University of Science and Technology (Platinum Sponsor)

Location: Roselle Junior 4711

Abstract: 

The development of control and optimization spans several decades and has evolved through significant contributions from various fields, including mathematics, engineering, economics, and operations research. Today, control and optimization continue to advance with the integration of networks, artificial intelligence, and machine learning. They are applied to address complex real-world problems across diverse domains while encountering some emerging challenges. To tackle these challenges, some new theories, methodologies, and algorithms are constantly being developed. The workshop is on recent advances of the related new theories, methodologies, and algorithms. The aim is to brainstorm on a couple of focused topics related to learning-based control and multiagent systems but not limited to these topics.

Lecture Schedule:

09:00--12:00	Session 1	Chair
09:00--09:10	Welcoming Remarks
09:10--09:40	Lecture 1: Online Learning-Based Adaptive Zero-Sum Differential Game Theory Prof. Lei Guo	Li Qiu
09:40--10:10	Lecture 2: Risk Sensitive Control for Quantum Linear Systems Prof. Ian R. Peterson	Li Qiu
10:10--10:40	Lecture 3: Approximate Byzantine Consensus via Multi-hop Communication Prof. Hideaki Ishii	Li Qiu
10:40--11:00	Break
11:00--11:30	Lecture 4: Decentralized Control and Optimization Based on Optimal Control Prof. Huanshui Zhang	Minyue Fu
11:30--12:00	Lecture 5: Distributed Optimization for Addressing Emerging Challenges in the Energy Sector Prof. Maria Prandini	Minyue Fu
12:00--14:20	Lunch Break
14:00--15:30	Session 2	Chair
14:00--14:30	Lecture 6: Contraction Analysis for Networked Optimization and Control Prof. Francesco Bullo	Kemin Zhou
14:30--15:00	Lecture 7: Fast Reinforcement Learning for Continuous Markovian Decision Processes Prof. Minyue Fu	Kemin Zhou
15:00--15:30	Lecture 8: The Importance of Phase in Systems Prof. Li Qiu	Kemin Zhou