Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Doostmohammadian, Mohammadreza"'
Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization problems described over a network of nodes and proposes a computationall
Externí odkaz:
http://arxiv.org/abs/2408.02269
Decentralized algorithms have gained substantial interest owing to advancements in cloud computing, Internet of Things (IoT), intelligent transportation networks, and parallel processing over sensor networks. The convergence of such algorithms is dir
Externí odkaz:
http://arxiv.org/abs/2407.01460
Autor:
Doostmohammadian, Mohammadreza, Qureshi, Muhammad I., Khalesi, Mohammad Hossein, Rabiee, Hamid R., Khan, Usman A.
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 subject to quantization. Each node possesses a private local cost functio
Externí odkaz:
http://arxiv.org/abs/2406.00621
Autor:
Doostmohammadian, Mohammadreza, Aghasi, Alireza, Pirani, Mohammad, Nekouei, Ehsan, Zarrabi, Houman, Keypour, Reza, Rikos, Apostolos I., Johansson, Karl H.
Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource alloc
Externí odkaz:
http://arxiv.org/abs/2401.15607
Distributed allocation finds applications in many scenarios including CPU scheduling, distributed energy resource management, and networked coverage control. In this paper, we propose a fast convergent optimization algorithm with a tunable rate using
Externí odkaz:
http://arxiv.org/abs/2401.15598
Autor:
Doostmohammadian, Mohammadreza, Jiang, Wei, Liaquat, Muwahida, Aghasi, Alireza, Zarrabi, Houman
Publikováno v:
IEEE Transactions on Automation science and Engineering 2024
We consider a discrete-time model of continuous-time distributed optimization over dynamic directed-graphs (digraphs) with applications to distributed learning. Our optimization algorithm works over general strongly connected dynamic networks under s
Externí odkaz:
http://arxiv.org/abs/2311.07939
Autor:
Doostmohammadian, Mohammadreza, Aghasi, Alireza, Vrakopoulou, Maria, Rabiee, Hamid R., Khan, Usman A., Charalambou, Themistoklis
Publikováno v:
SCL 2023
This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network. In this setup, coupling constraint refers to resource-demand balance which is preserved at all-times.
Externí odkaz:
http://arxiv.org/abs/2310.18225
Publikováno v:
SNAM 2023
Understanding the impact of network clustering and small-world properties on epidemic spread can be crucial in developing effective strategies for managing and controlling infectious diseases. Particularly in this work, we study the impact of these n
Externí odkaz:
http://arxiv.org/abs/2310.12594
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. In other words, the idea is to optimally allocate the generated and reserved
Externí odkaz:
http://arxiv.org/abs/2308.11263
This paper considers distributed optimization algorithms, with application in binary classification via distributed support-vector-machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a consensus-constraint
Externí odkaz:
http://arxiv.org/abs/2304.06667