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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2409.03188 |
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| _version_ | 1866912015459549184 |
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| author | Lin, Qinlong Liu, Yang Lu, Jianquan Gui, Weihua |
| author_facet | Lin, Qinlong Liu, Yang Lu, Jianquan Gui, Weihua |
| contents | In this paper, we propose two novel multi-agent systems for the resource allocation problems (RAPs) and consensus-based distributed optimization problems. Different from existing distributed optimal approaches, we propose the new time-base generators (TBGs) for predefined-time non-convex optimization. Leveraging the proposed time-base generator, we study the roughness and boundedness of Lyapunov function based on TBGs. We prove that our approach achieves predefined-time approximate convergence to the optimal solution if the cost functions exhibit non-strongly convex or even non-convex characteristics. Furthermore, we prove that our approaches converge to the optimal solution if cost functions are generalized smoothness, and exhibit faster convergence rate and CPU speed. Finally, we present numerous numerical simulation examples to confirm the effectiveness of our approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03188 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predefined-time distributed non-convex optimization via a time-base generator Lin, Qinlong Liu, Yang Lu, Jianquan Gui, Weihua Optimization and Control In this paper, we propose two novel multi-agent systems for the resource allocation problems (RAPs) and consensus-based distributed optimization problems. Different from existing distributed optimal approaches, we propose the new time-base generators (TBGs) for predefined-time non-convex optimization. Leveraging the proposed time-base generator, we study the roughness and boundedness of Lyapunov function based on TBGs. We prove that our approach achieves predefined-time approximate convergence to the optimal solution if the cost functions exhibit non-strongly convex or even non-convex characteristics. Furthermore, we prove that our approaches converge to the optimal solution if cost functions are generalized smoothness, and exhibit faster convergence rate and CPU speed. Finally, we present numerous numerical simulation examples to confirm the effectiveness of our approaches. |
| title | Predefined-time distributed non-convex optimization via a time-base generator |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2409.03188 |