Convergence of distributed approximate subgradient method for minimizing convex function with convex functional constraints
In this paper, we investigate the distributed approximate subgradient-type method for minimizing a sum of differentiable and non-differentiable convex functions subject to nondifferentiable convex functional constraints in a Euclidean space. We establish the convergence of the sequence generated by...
| Published in: | AIMS Mathematics |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-06-01
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| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2024934?viewType=HTML |
| Summary: | In this paper, we investigate the distributed approximate subgradient-type method for minimizing a sum of differentiable and non-differentiable convex functions subject to nondifferentiable convex functional constraints in a Euclidean space. We establish the convergence of the sequence generated by our method to an optimal solution of the problem under consideration. Moreover, we derive a convergence rate of order $ \mathcal{O}(N^{1-a}) $ for the objective function values, where $ a\in (0.5, 1) $. Finally, we provide a numerical example illustrating the effectiveness of the proposed method. |
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| ISSN: | 2473-6988 |
