17 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…
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…
Unified Communication Compression Beyond Global Error Bounds for Distributed Nonconvex Optimization
Haonan Wang, Minghui Liwang, Yiguang Hong +2
In this paper, we propose a unified compression algorithm for distributed nonconvex opitmization with both the locally- and globally-bounded communication compressors, including 1-…
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…
Nesterov Accelerated Distributed Optimization with Efficient Quantized Communication
Ruochen Wu, Xu Du, Karl H. Johansson +1
In modern large-scale networked systems, rapidly solving optimization problems while utilizing communication resources efficiently is critical for addressing complex tasks. In this…