Exponential convergence of a distributed divide-and-conquer algorithm for constrained convex optimization on networks

We propose a divide-and-conquer (DAC) algorithm for constrained convex optimization over networks, where the global objective is the sum of local objectives attached to individual agents. The algorithm is fully distributed: each iteration solves local subproblems around selected fusion centers and c...

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Podrobná bibliografie
Vydáno v:	Expositiones mathematicae Ročník 43; číslo 6; s. 125740
Hlavní autoři:	Emirov, Nazar, Song, Guohui, Sun, Qiyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier GmbH 01.12.2025
Témata:	Constrained convex optimization on network Distributed and decentralized algorithm Divide and conquer algorithm secondary Constrained convex optimization on network Distributed and decentralized algorithm Divide and conquer algorithm primary
ISSN:	0723-0869
On-line přístup:	Získat plný text
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Popis
Shrnutí:	We propose a divide-and-conquer (DAC) algorithm for constrained convex optimization over networks, where the global objective is the sum of local objectives attached to individual agents. The algorithm is fully distributed: each iteration solves local subproblems around selected fusion centers and coordinates only with neighboring fusion centers. Under standard assumptions of smoothness, strong convexity, and locality on the objective function, together with polynomial growth conditions on the underlying graph, we establish exponential convergence of the DAC iterations and derive explicit bounds for both exact and inexact local solvers. Numerical experiments on three representative losses (L2 distance, quadratic, and entropy) confirm the theory and demonstrate scalability and effectiveness.
ISSN:	0723-0869
DOI:	10.1016/j.exmath.2025.125740