20 papers
Decentralized Linearized Consensus ADMM with Efficient Quantized Communication
Boyu Han, Xu Du, Karl H. Johansson +1
Distributed optimization offers significant advantages over centralized methods in terms of scalability and robustness when solving large-scale problems. In this paper, we propose…
On Optimal Event-Triggered Distributed Control for Stochastic Multi-Agent Systems via Reinforcement Learning
Ziming Wang, Bingbing Li, Karl H. Johansson +1
We propose a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties. Unlike existing methods, duri…
CADMM-Prox: A Bi-level Consensus ADMM for Non-smooth Non-convex Distributed Consensus Optimization
Xu Du, Shuting Wu, Karl H. Johansson +1
Non-smooth and non-convex optimization problems are pervasive in machine learning, control, and signal processing, due to the need for sparse solutions and the inherently non-conve…
Cooperative Switched Formation Control of Autonomous Vehicles: An Event-triggered Approach to Input Saturation and Time-delay Challenges
Ziming Wang, Guanxuan Jiang, Yihuai Zhang +2
This paper presents a collaborative adaptive formation control framework for autonomous vehicles (AVs), that explicitly handles system uncertainties, input saturation, and communic…
A Global Convergence Analysis of Consensus ALADIN for Convex Optimization
Xu Du, Shuting Wu, Karl H. Johansson +1
Distributed optimization problems are pervasive in machine learning and optimal control. In this paper, we study smooth strongly convex distributed consensus optimization problems.…
Distributed and Decentralized Optimization Algorithms via Consensus ALADIN
Xu Du, Jingzhe Wang, Karl H. Johansson +1
Distributed optimization has found widespread applications in smart grids, optimal control, and machine learning. This paper studies distributed consensus optimization. We extend t…