Exponential convergence of distributed primal–dual convex optimization algorithm without strong convexity

This paper establishes exponential convergence rates for a class of primal–dual gradient algorithms in distributed optimization without strong convexity. The convergence analysis is based on a carefully constructed Lyapunov function. By evaluating metric subregularity of the primal–dual gradient map...

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Bibliographic Details
Published in:Automatica (Oxford) 2019-07, Vol.105, p.298-306
Main Authors: Liang, Shu, Wang, Le Yi, Yin, George
Format: Article
Language:English
Subjects:
Convex optimization without strong convexity
Distributed optimization
Exponential convergence
Metric subregularity
Primal–dual algorithm
Rate of convergence
Variational analysis
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Summary:This paper establishes exponential convergence rates for a class of primal–dual gradient algorithms in distributed optimization without strong convexity. The convergence analysis is based on a carefully constructed Lyapunov function. By evaluating metric subregularity of the primal–dual gradient map, we present a general criterion under which the algorithm achieves exponential convergence. To facilitate practical applications of this criterion, several simplified sufficient conditions are derived. We also prove that although these results are developed for the continuous-time algorithms, they carry over in a parallel manner to the discrete-time algorithms constructed by using Euler’s approximation method.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2019.04.004