4 citations · 11 across the 46 of their papers we have counts for
46 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…
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…
Lightweight Real-Time ALADIN for Distributed Optimization
Yifei Wang, Xuhui Feng, Shimin Pan +3
This paper presents a real-time computational framework for multi-node distributed optimization by extending the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN)…
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…
Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
Xu Du, Boyu Han, Ivano Notarnicola +2
This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integr…
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…
Average Consensus with Dynamic Quantization Framing and Finite-Time Termination over Limited-Bandwidth Directed Networks
Evagoras Makridis, Gabriele Oliva, Apostolos I. Rikos +1
This paper proposes a deterministic distributed algorithm, referred to as PP-ACDC, that achieves exact average consensus over possibly unbalanced directed graphs using only a fixed…
Average Consensus with Dynamic Compression in Bandwidth-Limited Directed Networks
Evagoras Makridis, Gabriele Oliva, Apostolos I. Rikos +1
In this paper, the average consensus problem has been considered for directed unbalanced networks under finite bit-rate communication. We propose the Push-Pull Average Consensus al…
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…
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…
HARQ-based Quantized Average Consensus over Unreliable Directed Network Topologies
Neofytos Charalampous, Evagoras Makridis, Apostolos I. Rikos +1
In this paper, we propose a distributed algorithm (herein called HARQ-QAC) that enables nodes to calculate the average of their initial states by exchanging quantized messages over…
Convergence Theory of Flexible ALADIN for Distributed Optimization
Xu Du, Xiaohua Zhou, Shijie Zhu
The Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method is a cutting-edge distributed optimization algorithm known for its superior numerical performance. It…
Fixed-Relative-Switched Threshold Strategies for Consensus Tracking Control of Nonlinear Multiagent Systems
Ziming Wang, Yun Gao, Apostolos I. Rikos +2
This paper investigates event-triggered consensus tracking in nonlinear semi-strict-feedback multi-agent systems involving one leader and multiple followers. We first employ radial…
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…
Network-Based Epidemic Control Through Optimal Travel and Quarantine Management
Mahtab Talaei, Apostolos I. Rikos, Alex Olshevsky +2
Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two d…
Max-Consensus with Deterministic Convergence in Directed Graphs with Unreliable Communication Links
Apostolos I. Rikos, Jiaqi Hu, Themistoklis Charalambous +1
We present DMaC, a novel distributed, finite-time algorithm that guarantees max-consensus in directed networks with unreliable communication links experiencing packet drops. Unlike…