Exact spectral-like gradient method for distributed optimization

Authors Dušan Jakovetić, Nataša Krejić, Nataša Krklec Jerinkić
Title Exact spectral-like gradient method for distributed optimization
Abstract Since the initial proposal in the late 80s, spectral gradient methods continue to receive significant attention, especially due to their excellent numerical performance on various large scale applications. However, to date, they have not been sufficiently explored in the context of distributed optimization. In this paper, we consider unconstrained distributed optimization problems where n nodes constitute an arbitrary connected network and collaboratively minimize the sum of their local convex cost functions. In this setting, building from existing exact distributed gradient methods, we propose a novel exact distributed gradient method wherein nodes’ step-sizes are designed according to the novel rules akin to those in spectral gradient methods. We refer to the proposed method as Distributed Spectral Gradient method. The method exhibits R-linear convergence under standard assumptions for the nodes’ local costs and safe-guarding on the algorithm step-sizes. We illustrate the method’s performance through simulation examples.
Journal Computational Optimization and Applications, Springer
Volume 74
Number -
Pages 703 - 728
Year 2019
Url https://zenodo.org/record/3591087#.XiB2ty17HUI
DOI https://doi.org/10.1007/s10589-019-00131-8