Momentum-based Distributed Resource Scheduling Optimization Subject to Sector-Bound Nonlinearity and Latency

2025-03-08

AI Summary

This paper addresses the challenges of distributed resource scheduling in dynamic multi-agent networks—namely, sector-bounded nonlinearities, communication delays, time-varying topologies, and real-time feasibility guarantees. Methodologically, it proposes a momentum-enhanced distributed optimization algorithm that pioneers the integration of accelerated momentum with gradient tracking; auxiliary variables are introduced to enforce full-time satisfaction of coupled constraints. Leveraging sector-bounded nonlinearity theory, time-varying graph theory, and delay-compensation techniques, the algorithm ensures robustness against nonlinearities, convergence under uniformly connected graphs, and tolerance to arbitrary bounded delays. Theoretically, it is proven to achieve global convergence under quantified nonlinearities and dynamic topologies, while maintaining strict constraint feasibility at all times—even upon abrupt interruption. Experiments demonstrate substantial improvements in convergence speed and deployment robustness compared to state-of-the-art approaches.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Optimizes resource allocation with momentum-based distributed algorithm.
• Handles sector-bound nonlinearity and latency in dynamic networks.
• Ensures all-time feasibility and convergence in mobile multi-agent systems.

Innovation

• Momentum-based gradient-tracking for resource allocation
• All-time feasible solution with coupling constraints
• Handles nonlinearity and latency in dynamic networks