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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.16845 |
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| _version_ | 1866917964514590720 |
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| author | Wang, Yaowen Mo, Lipo Zuo, Min Zheng, Yuanshi |
| author_facet | Wang, Yaowen Mo, Lipo Zuo, Min Zheng, Yuanshi |
| contents | This paper mainly addresses the distributed online optimization problem where the local objective functions are assumed to be convex or non-convex. First, the distributed algorithms are proposed for the convex and non-convex situations, where the one-point residual feedback technology is introduced to estimate gradient of local objective functions. Then the regret bounds of the proposed algorithms are derived respectively under the assumption that the local objective functions are Lipschitz or smooth, which implies that the regrets are sublinear. Finally, we give two numerical examples of distributed convex optimization and distributed resources allocation problem to illustrate the effectiveness of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One-Point Residual Feedback Algorithms for Distributed Online Convex and Non-convex Optimization Wang, Yaowen Mo, Lipo Zuo, Min Zheng, Yuanshi Optimization and Control Systems and Control This paper mainly addresses the distributed online optimization problem where the local objective functions are assumed to be convex or non-convex. First, the distributed algorithms are proposed for the convex and non-convex situations, where the one-point residual feedback technology is introduced to estimate gradient of local objective functions. Then the regret bounds of the proposed algorithms are derived respectively under the assumption that the local objective functions are Lipschitz or smooth, which implies that the regrets are sublinear. Finally, we give two numerical examples of distributed convex optimization and distributed resources allocation problem to illustrate the effectiveness of the proposed algorithm. |
| title | One-Point Residual Feedback Algorithms for Distributed Online Convex and Non-convex Optimization |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2503.16845 |