A Framework for Distributed Personalized Optimization in Cyber-Physical Systems via Multi-Parameter Fractional Dynamics

This paper aims at solving the distributed personalized optimization problem in cyber-physical systems by combining the fractional dynamics. To fully utilize the advantages of the basic primal-dual method and its different variants, several parameters are introduced, which brings a unified framework. Along with the continuous time algorithms, the discrete time algorithms are constructed. The convergence is analyzed by using the Lyapunov stability theory. To demonstrate the effectiveness and efficiency of the elaborated algorithms, a series of examples are provided.