no code implementations • 28 Jan 2024 • Mohammadreza Doostmohammadian, Alireza Aghasi
The proposed algorithm is all-time feasible, implying that at any termination time of the algorithm, the resource-demand feasibility holds.
no code implementations • 28 Jan 2024 • Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani, Ehsan Nekouei, Houman Zarrabi, Reza Keypour, Apostolos I. Rikos, Karl H. Johansson
This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems.
no code implementations • 30 Nov 2023 • Mohammadreza Doostmohammadian, Alireza Aghasi
Efficient resource allocation and scheduling algorithms are essential for various distributed applications, ranging from wireless networks and cloud computing platforms to autonomous multi-agent systems and swarm robotic networks.
no code implementations • 14 Nov 2023 • Mohammadreza Doostmohammadian, Wei Jiang, Muwahida Liaquat, Alireza Aghasi, Houman Zarrabi
This work, particularly, is an improvement over existing stochastic-weight undirected networks in case of link removal or packet drops.
no code implementations • 27 Oct 2023 • Mohammadreza Doostmohammadian, Alireza Aghasi, Maria Vrakopoulou, Hamid R. Rabiee, Usman A. Khan, Themistoklis Charalambou
This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network.
no code implementations • 22 Aug 2023 • Mohammadreza Doostmohammadian
In this work, we propose distributed and networked energy management scenarios to optimize the production and reservation of energy among a set of distributed energy nodes.
no code implementations • 13 Apr 2023 • Mohammadreza Doostmohammadian, Alireza Aghasi, Houman Zarrabi
The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions.
no code implementations • 15 Feb 2023 • Mohammadreza Doostmohammadian, Mohammad Pirani, Usman A. Khan
The tracking part is based on linear time-difference-of-arrival (TDOA) measurement proposed in our previous works.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Alireza Aghasi, Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Karl H. Johansson, Themistoklis Charalambous
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Usman A. Khan, Alireza Aghasi, Themistoklis Charalambous
This paper considers distributed resource allocation and sum-preserving constrained optimization over lossy networks, where the links are unreliable and subject to packet drops.
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Wei Jiang, Themistoklis Charalambous
Every node locally updates its state toward optimizing a global allocation cost function via received information of its neighbouring nodes even when the data exchange over the network is heterogeneously delayed at different links.
no code implementations • 26 Apr 2022 • Mohammadreza Doostmohammadian, Themistoklis Charalambous
We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target.
no code implementations • 4 Apr 2022 • Mohammadreza Doostmohammadian, Themistoklis Charalambous
Instead, only instantaneous deviation of the residuals raises the alarm in the stateless case without considering the history of the sensor outputs and estimation data.
no code implementations • 28 Mar 2022 • Mohammadreza Doostmohammadian, Maria Vrakopoulou, Alireza Aghasi, Themistoklis Charalambous
Motivated by recent development in networking and parallel data-processing, we consider a distributed and localized finite-sum (or fixed-sum) allocation technique to solve resource-constrained convex optimization problems over multi-agent networks (MANs).
no code implementations • 20 Sep 2021 • Mohammadreza Doostmohammadian, Houman Zarrabi, Hamid R. Rabiee, Usman A. Khan, Themistoklis Charalambous
First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state.
no code implementations • 10 Sep 2021 • Mohammadreza Doostmohammadian, Alireza Aghasi, Maria Vrakopoulou, Themistoklis Charalambous
A general nonlinear $1$st-order consensus-based solution for distributed constrained convex optimization is proposed with network resource allocation applications.
no code implementations • 22 May 2021 • Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah, Hamid R. Rabiee, Usman A. Khan
Observability and estimation are closely tied to the system structure, which can be visualized as a system graph--a graph that captures the inter-dependencies within the state variables.
no code implementations • 22 May 2021 • Mohammadreza Doostmohammadian, Themistoklis Charalambous, Miadreza Shafie-khah, Nader Meskin, Usman A. Khan
This paper considers distributed estimation of linear systems when the state observations are corrupted with Gaussian noise of unbounded support and under possible random adversarial attacks.
no code implementations • 1 Apr 2021 • Mohammadreza Doostmohammadian, Alireza Aghasi, Themistoklis Charalambous, Usman A. Khan
In this paper, we consider the binary classification problem via distributed Support-Vector-Machines (SVM), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database.
no code implementations • 1 Apr 2021 • Mohammadreza Doostmohammadian, Usman A. Khan, Mohammad Pirani, Themistoklis Charalambous
Classical distributed estimation scenarios typically assume timely and reliable exchanges of information over the sensor network.
no code implementations • 15 Dec 2020 • Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani, Ehsan Nekouei, Usman A. Khan, Themistoklis Charalambous
The idea is to optimally allocate the resources among the group of agents by minimizing the overall cost function subject to fixed sum of resources.