4 citations · 11 across the 46 of their papers we have counts for
33 papers · 1 filter
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…
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 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…
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…
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…
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…
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…
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence
Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki +3
We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale n…
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication
Apostolos I. Rikos, Themistoklis Charalambous, Christoforos N. Hadjicostis +1
We consider the problems of computing the average degree and the size of a given network in a distributed fashion under quantized communication. We present two distributed algorith…
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…
Distributed CPU Scheduling Subject to Nonlinear Constraints
Mohammadreza Doostmohammadian, Alireza Aghasi, Apostolos I. Rikos +5
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) tha…
Finite Time Privacy Preserving Quantized Average Consensus with Transmission Stopping
Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Due to their flexibility, battery powered or energy-harvesting wireless networks are employed in diverse applications. Securing data transmissions between wireless devises is of cr…
Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks
Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
In this paper, we focus on the problem of data sharing over a wireless computer network (i.e., a wireless grid). Given a set of available data, we present a distributed algorithm w…