11 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…
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems
Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani +5
Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis o…
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication
Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous +1
In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topolog…
Asynchronous Distributed Optimization via ADMM with Efficient Communication
Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous +1
In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each node is endowed with a convex local cost function, and is able to communicate with…