Decentralized Optimization with Mixed Affine Constraints
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915773892526080 |
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| author | Yarmoshik, Demyan Nguyen, Nhat Trung Rogozin, Alexander Gasnikov, Alexander |
| author_facet | Yarmoshik, Demyan Nguyen, Nhat Trung Rogozin, Alexander Gasnikov, Alexander |
| contents | This paper considers decentralized optimization of convex functions with mixed affine equality constraints involving both local and global variables. Constraints on global variables may vary across different nodes in the network, while local variables are subject to coupled and node-specific constraints. Such problem formulations arise in machine learning applications, including federated learning and multi-task learning, as well as in resource allocation and distributed control. We analyze this problem under smooth and non-smooth assumptions, considering both strongly convex and general convex objective functions. Our main contribution is an optimal algorithm for the smooth, strongly convex regime, whose convergence rate matches established lower complexity bounds. We further provide near-optimal methods for the remaining cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04479 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decentralized Optimization with Mixed Affine Constraints Yarmoshik, Demyan Nguyen, Nhat Trung Rogozin, Alexander Gasnikov, Alexander Optimization and Control This paper considers decentralized optimization of convex functions with mixed affine equality constraints involving both local and global variables. Constraints on global variables may vary across different nodes in the network, while local variables are subject to coupled and node-specific constraints. Such problem formulations arise in machine learning applications, including federated learning and multi-task learning, as well as in resource allocation and distributed control. We analyze this problem under smooth and non-smooth assumptions, considering both strongly convex and general convex objective functions. Our main contribution is an optimal algorithm for the smooth, strongly convex regime, whose convergence rate matches established lower complexity bounds. We further provide near-optimal methods for the remaining cases. |
| title | Decentralized Optimization with Mixed Affine Constraints |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2602.04479 |