Exact spectral-like gradient method for distributed optimization

Dušan Jakovetić (), Nataša Krejić () and Nataša Krklec Jerinkić ()
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Dušan Jakovetić: University of Novi Sad
Nataša Krejić: University of Novi Sad
Nataša Krklec Jerinkić: University of Novi Sad

Computational Optimization and Applications, 2019, vol. 74, issue 3, No 5, 703-728

Abstract: 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 safeguarding on the algorithm step-sizes. We illustrate the method’s performance through simulation examples.

Keywords: Distributed optimization; Spectral gradient; R-linear convergence; 90C25; 90C53; 65K05 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10589-019-00131-8

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