Nabla fractional distributed optimization algorithms over undirected/directed graphs

Abstract

The primary focus of this paper centers on investigating unconstrained distributed optimization problems over undirected or directed graphs. One noteworthy departure from current distributed optimization algorithms in the continuous-time domain is the integration of nabla fractional calculus, which augments algorithmic performance by reducing iterative complexity. Through rigorous analysis, this paper demonstrates that the two algorithms presented converge at the Mittag–Leffler rate to the precise solution of a distributed optimization problem over a connected undirected graph or a connected balanced directed graph with strongly convex and smooth objective functions. The research findings provide valuable insights into the potential utility of nabla fractional calculus in distributed optimization problems, highlighting the possibility of enhancing the efficiency and effectiveness of distributed optimization algorithms.