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    Area of Science:

    • Distributed Systems
    • Optimization Algorithms
    • Control Theory

    Background:

    • Resource allocation problems require efficient distributed algorithms.
    • Event-triggered communication enhances efficiency and privacy over periodic methods.
    • Minimizing the sum of objective functions is a key challenge.

    Purpose of the Study:

    • To design distributed algorithms for resource allocation using event-triggered communication.
    • To achieve optimal resource allocation by minimizing the sum of objective functions.
    • To improve communication efficiency and maintain data privacy.

    Main Methods:

    • Development of distributed algorithms with event-triggered communication protocols.
    • Implementation of a novel technical lemma and a universal scalar function for convergence analysis.
    • Establishment of convergence and linear convergence rates under mild assumptions.

    Main Results:

    • Proposed algorithms significantly reduce communication times compared to periodic methods (e.g., ADMM, Mirror-P-EXTRA).
    • Event-triggered algorithms demonstrate competitive convergence speeds.
    • Numerical experiments on the IEEE 118-bus power system validate the findings.

    Conclusions:

    • Event-triggered communication is a viable strategy for efficient distributed resource allocation.
    • Periodic communication in existing algorithms may involve redundant transmissions.
    • The proposed algorithms offer a more efficient and privacy-preserving approach to resource allocation.