Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929748571062272 |
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| author | Doostmohammadian, Mohammadreza Gabidullina, Zulfiya R. Rabiee, Hamid R. |
| author_facet | Doostmohammadian, Mohammadreza Gabidullina, Zulfiya R. Rabiee, Hamid R. |
| contents | This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an auxiliary variable to add momentum towards the optimal state. We prove that this solution is all-time feasible, implying that the coupling constraint always holds along the algorithm iterative procedure; therefore, the algorithm can be terminated at any time. This is in contrast to the ADMM-based solutions that meet constraint feasibility asymptotically. Further, we show that the proposed algorithm can handle possible link nonlinearity due to logarithmically-quantized data transmission (or any sign-preserving odd sector-bound nonlinear mapping). We prove convergence over uniformly-connected dynamic networks (i.e., a hybrid setup) that may occur in mobile and time-varying multi-agent networks. Further, the latency issue over the network is addressed by proposing delay-tolerant solutions. To our best knowledge, accelerated momentum-based convergence, nonlinear linking, all-time feasibility, uniform network connectivity, and handling (possible) time delays are not altogether addressed in the literature. These contributions make our solution practical in many real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency Doostmohammadian, Mohammadreza Gabidullina, Zulfiya R. Rabiee, Hamid R. Systems and Control Distributed, Parallel, and Cluster Computing Multiagent Systems Signal Processing Optimization and Control This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an auxiliary variable to add momentum towards the optimal state. We prove that this solution is all-time feasible, implying that the coupling constraint always holds along the algorithm iterative procedure; therefore, the algorithm can be terminated at any time. This is in contrast to the ADMM-based solutions that meet constraint feasibility asymptotically. Further, we show that the proposed algorithm can handle possible link nonlinearity due to logarithmically-quantized data transmission (or any sign-preserving odd sector-bound nonlinear mapping). We prove convergence over uniformly-connected dynamic networks (i.e., a hybrid setup) that may occur in mobile and time-varying multi-agent networks. Further, the latency issue over the network is addressed by proposing delay-tolerant solutions. To our best knowledge, accelerated momentum-based convergence, nonlinear linking, all-time feasibility, uniform network connectivity, and handling (possible) time delays are not altogether addressed in the literature. These contributions make our solution practical in many real-world applications. |
| title | Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency |
| topic | Systems and Control Distributed, Parallel, and Cluster Computing Multiagent Systems Signal Processing Optimization and Control |
| url | https://arxiv.org/abs/2503.06167 |