13 citations · 16 across the 23 of their papers we have counts for
23 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…
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…
Distributed Optimization with Nesterov Momentum and Finite-Time Quantized Averaging over Directed Networks: Extended Version
Ruochen Wu, Xu Du, Karl H. Johansson +2
In modern large-scale networked systems, rapidly solving optimization problems while utilizing communication resources efficiently is critical for addressing complex tasks. However…
Mix-CALADIN: A Distributed Algorithm for Consensus Mixed-Integer Optimization
Boyu Han, Xu Du, Karl H. Johansson +1
This paper addresses distributed consensus optimization problems with mixed-integer variables, with a specific focus on Boolean variables. We introduce a novel distributed algorith…
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-…
Distributed Coordination Algorithms with Efficient Communication for Open Multi-Agent Systems with Dynamic Communication Links and Processing Delays: Extended Version
Jiaqi Hu, Karl H. Johansson, Apostolos I. Rikos
In this paper we focus on the distributed quantized average consensus problem in open multi-agent systems consisting of dynamic directed communication links among active nodes. We…
Communication-Efficient Learning for Satellite Constellations
Ruxandra-Stefania Tudose, Moritz H. W. Grüss, Grace Ra Kim +2
Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems…
Decentralized Optimization via RC-ALADIN with Efficient Quantized Communication
Xu Du, Karl H. Johansson, Apostolos I. Rikos
In this paper, we investigate the problem of decentralized consensus optimization over directed graphs with limited communication bandwidth. We introduce a novel decentralized opti…
Distributed Quantized Average Consensus in Open Multi-Agent Systems with Dynamic Communication Links
Jiaqi Hu, Karl H. Johansson, Apostolos I. Rikos
In this paper, we focus on the distributed quantized average consensus problem in open multi-agent systems consisting of communication links that change dynamically over time. Open…
Distributed Optimization and Learning for Automated Stepsize Selection with Finite Time Coordination
Apostolos I. Rikos, Nicola Bastianello, Themistoklis Charalambous +1
Distributed optimization and learning algorithms are designed to operate over large scale networks enabling processing of vast amounts of data effectively and efficiently. One of t…
Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson +1
We address distributed learning problems over undirected networks. Specifically, we focus on designing a novel ADMM-based algorithm that is jointly computation- and communication-e…
Personalized and Resilient Distributed Learning Through Opinion Dynamics
Luca Ballotta, Nicola Bastianello, Riccardo M. G. Ferrari +1
In this paper, we address two practical challenges of distributed learning in multi-agent network systems, namely personalization and resilience. Personalization is the need of het…
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping
Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous +1
In modern large-scale systems with sensor networks and IoT devices it is essential to collaboratively solve complex problems while utilizing network resources efficiently. In our p…
Optimization and Learning in Open Multi-Agent Systems
Diego Deplano, Nicola Bastianello, Mauro Franceschelli +1
Modern artificial intelligence relies on networks of agents that collect data, process information, and exchange it with neighbors to collaboratively solve optimization and learnin…
Communication-Efficient Stochastic Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson +1
We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direc…
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement
Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous +1
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the develop…
Asynchronous Distributed Learning with Quantized Finite-Time Coordination
Nicola Bastianello, Apostolos I. Rikos, Karl H. Johansson
In this paper we address distributed learning problems over peer-to-peer networks. In particular, we focus on the challenges of quantized communications, asynchrony, and stochastic…
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…
Robust Online Learning over Networks
Nicola Bastianello, Diego Deplano, Mauro Franceschelli +1
The recent deployment of multi-agent networks has enabled the distributed solution of learning problems, where agents cooperate to train a global model without sharing their local,…
Distributed Optimization with Quantized Gradient Descent
Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous +1
In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, n…