Accelerated Distributed Allocation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911766313697280 |
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| author | Doostmohammadian, Mohammadreza Aghasi, Alireza |
| author_facet | Doostmohammadian, Mohammadreza Aghasi, Alireza |
| contents | Distributed allocation finds applications in many scenarios including CPU scheduling, distributed energy resource management, and networked coverage control. In this paper, we propose a fast convergent optimization algorithm with a tunable rate using the signum function. The convergence rate of the proposed algorithm can be managed by changing two parameters. We prove convergence over uniformly-connected multi-agent networks. Therefore, the solution converges even if the network loses connectivity at some finite time intervals. The proposed algorithm is all-time feasible, implying that at any termination time of the algorithm, the resource-demand feasibility holds. This is in contrast to asymptotic feasibility in many dual formulation solutions (e.g., ADMM) that meet resource-demand feasibility over time and asymptotically. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerated Distributed Allocation Doostmohammadian, Mohammadreza Aghasi, Alireza Signal Processing Multiagent Systems Systems and Control Optimization and Control Distributed allocation finds applications in many scenarios including CPU scheduling, distributed energy resource management, and networked coverage control. In this paper, we propose a fast convergent optimization algorithm with a tunable rate using the signum function. The convergence rate of the proposed algorithm can be managed by changing two parameters. We prove convergence over uniformly-connected multi-agent networks. Therefore, the solution converges even if the network loses connectivity at some finite time intervals. The proposed algorithm is all-time feasible, implying that at any termination time of the algorithm, the resource-demand feasibility holds. This is in contrast to asymptotic feasibility in many dual formulation solutions (e.g., ADMM) that meet resource-demand feasibility over time and asymptotically. |
| title | Accelerated Distributed Allocation |
| topic | Signal Processing Multiagent Systems Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2401.15598 |