77 citations · 329 across the 57 of their papers we have counts for
64 papers
Low-Bit Distributed Constrained Optimization with Application to Optimal Scheduling and Allocation
Mohammadreza Doostmohammadian, Sergio Pequito
This paper proposes a distributed resource allocation algorithm in which each agent communicates only the sign of its local cost gradient, a single bit per directed link per iterat…
Adding Heavy-Ball Momentum to Distributed Constrained Optimization for Energy Resource Management Subject to Time-Delays and Quantization
Mohammadreza Doostmohammadian, Sergio Pequito
This paper studies a hybrid discrete-time signed gradient-consensus method with heavy-ball momentum, paired communication delays, and fixed logarithmic quantization. The method pre…
Distributed Algorithms for Filtering, Estimation, and Fault Detection over Cyber-Physical-Systems: A Tutorial and Survey
Mohammadreza Doostmohammadian, Mahdi Shamsi, Hadi Zayyani +4
This survey provides overview of distributed estimation, filtering, and fault detection techniques in the context of cyber-physical systems (CPS). To establish a strong foundation,…
Resilient Monitoring of Social Dynamical Systems through Collaborative Multi-Agent Networks under Latency
Mohammadreza Doostmohammadian, Sergio Pequito
Social dynamical networks significantly influence contemporary digital landscapes, affecting realms from social activism to public policy formulation. This paper investigates the u…
Resilient Consensus-Based Target Tracking under False Data Injection Attacks in Multi-Agent Networks
Amir Ahmad Ghods, Mohammadreza Doostmohammadian
Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynam…
Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap
Mohammadreza Doostmohammadian, Amir Ahmad Ghods, Alireza Aghasi +2
In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learn…
Distributed Observer-based Fault Detection over Intelligent Networked Multi-Vehicle Systems
Mohammadreza Doostmohammadian, Hamid R. Rabiee
Decentralized strategies are of interest for local decision-making over multi-vehicle networks. This paper studies mixed traffic networks of human-driven and autonomous vehicles wi…
Impact of Clustering on the Observability and Controllability of Complex Networks
Mohammadreza Doostmohammadian, Hamid R. Rabiee
The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale-Free (SF) networks…
Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
In the rapidly evolving research on artificial intelligence (AI) the demand for fast, computationally efficient, and scalable solutions has increased in recent years. The problem o…
Distributed Allocation and Resource Scheduling Algorithms Resilient to Link Failure
Mohammadreza Doostmohammadian, Sergio Pequito
Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Con…
Delay-Tolerant Augmented-Consensus-based Distributed Directed Optimization
Mohammadreza Doostmohammadian, Narahari Kasagatta Ramesh, Alireza Aghasi
Distributed optimization finds applications in large-scale machine learning, data processing and classification over multi-agent networks. In real-world scenarios, the communicatio…
Distributed Automatic Generation Control subject to Ramp-Rate-Limits: Anytime Feasibility and Uniform Network-Connectivity
Mohammadreza Doostmohammadian, Hamid R. Rabiee
This paper considers automatic generation control over an information-sharing network of communicating generators as a multi-agent system. The optimization solution is distributed…
Momentum-based Accelerated Algorithm for Distributed Optimization under Sector-Bound Nonlinearity
Mohammadreza Doostmohammadian, Hamid R. Rabiee
Distributed optimization advances centralized machine learning methods by enabling parallel and decentralized learning processes over a network of computing nodes. This work provid…
On the Design of Resilient Distributed Single Time-Scale Estimators: A Graph-Theoretic Approach
Mohammadreza Doostmohammadian, Mohammad Pirani
Distributed estimation in interconnected systems has gained increasing attention due to its relevance in diverse applications such as sensor networks, autonomous vehicles, and clou…
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks
Evagoras Makridis, Gabriele Oliva, Kasagatta Ramesh Narahari +3
In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, w…
Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an au…
Distributed Observer Design for Tracking Platoon of Connected and Autonomous Vehicles
Mohammadreza Doostmohammadian, Hamid R. Rabiee
Intelligent transportation systems (ITS) aim to advance innovative strategies relating to different modes of transport, traffic management, and autonomous vehicles. This paper stud…
Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization
Mohammadreza Doostmohammadian, Alireza Aghasi, Hamid R. Rabiee
Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, thi…
Distributed Target Tracking based on Localization with Linear Time-Difference-of-Arrival Measurements: A Delay-Tolerant Networked Estimation Approach
Mohammadreza Doostmohammadian, Themistoklis Charalambous
This paper considers target tracking based on a beacon signal's time-difference-of-arrival (TDOA) to a group of cooperating sensors. The sensors receive a reflected signal from the…
Decentralized Mobile Target Tracking Using Consensus-Based Estimation with Nearly-Constant-Velocity Modeling
Amir Ahmad Ghods, Mohammadreza Doostmohammadian
Mobile target tracking is crucial in various applications such as surveillance and autonomous navigation. This study presents a decentralized tracking framework utilizing a Consens…
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks
Mohammadreza Doostmohammadian, Sérgio Pequito
Distributed optimization finds many applications in machine learning, signal processing, and control systems. In these real-world applications, the constraints of communication net…
Nonlinear Perturbation-based Non-Convex Optimization over Time-Varying Networks
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization pr…
How Clustering Affects the Convergence of Decentralized Optimization over Networks: A Monte-Carlo-based Approach
Mohammadreza Doostmohammadian, Shahaboddin Kharazmi, Hamid R. Rabiee
Decentralized algorithms have gained substantial interest owing to advancements in cloud computing, Internet of Things (IoT), intelligent transportation networks, and parallel proc…
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data
Mohammadreza Doostmohammadian, Muhammad I. Qureshi, Mohammad Hossein Khalesi +2
Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents sub…