Momentum-based distributed gradient tracking algorithms for distributed aggregative optimization over unbalanced directed graphs

This paper studies a distributed aggregative optimization problem over a directed graph with the row-stochastic weighted matrix. Different from the existing work on distributed optimization, the local cost function of each agent depends both on its local decision variable and on the sum of all funct...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Automatica (Oxford) Ročník 164; s. 111596
Hlavní autoři: Wang, Zhu, Wang, Dong, Lian, Jie, Ge, Hongwei, Wang, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2024
Témata:
Acceleration
Distributed aggregative optimization
Gradient tracking
Linear convergence
Row-stochastic weighted matrix
Row-stochastic weighted matrix
Gradient tracking
Distributed aggregative optimization
Linear convergence
Acceleration
ISSN:0005-1098, 1873-2836
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper studies a distributed aggregative optimization problem over a directed graph with the row-stochastic weighted matrix. Different from the existing work on distributed optimization, the local cost function of each agent depends both on its local decision variable and on the sum of all functions formed by the decision variables of all agents. Inspired by the distributed dynamic average consensus protocol, heavy-ball strategy, and Nesterov gradient descent method, a momentum-based distributed gradient tracking algorithm with a fixed step size is proposed to solve such a problem. Further, it is shown that the proposed algorithm has a linear convergence rate if the global cost function is strongly convex with the Lipschitz-continuous gradient. The upper bounds of the fixed step size and the momentum parameter are restricted by a sufficiently small positive constant, respectively. Finally, a numerical example is provided to verify the effectiveness of the findings.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2024.111596