Abstract
AbstractIn this paper, we consider a distributed optimization problem of minimizing sum of convex functions over the intersection of fixed-point constraints. We propose a distributed method for solving the problem. We prove the convergence of the generated sequence to the solution of the problem under certain assumption. We further discuss the convergence rate with an appropriate positive stepsize. A numerical experiment is given to show the effectiveness of the obtained theoretical result.
Funder
the development and promotion of science and technology talent project
khon kaen university
Thailand Science Research and Innovation
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Discrete Mathematics and Combinatorics,Analysis
Reference28 articles.
1. Bauschke, H.H., Combettes, P.L.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces, 2nd edn. Springer, New York (2017)
2. Beck, A.: First-Ordered Methods in Optimization. SIAM, Philadelphia (2017)
3. Bnouhachem, A.: A descent SQP alternating direction method for minimizing the sum of three convex functions. J. Nonlinear Var. Anal. 4, 469–482 (2020)
4. Bnouhachem, A., Qin, X.: An inertial proximal Peaceman–Rachford splitting method with SQP regularization for convex programming. J. Nonlinear Funct. Anal. (2020). https://doi.org/10.23952/jnfa.2020.50
5. Cauchy, A.L.: Methode generale pour la resolution des systemes d’equations simultanees. C. R. Acad. Sci. Paris 25, 536–538 (1847)
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献