iPiasco: Inertial Proximal Algorithm for Strongly Convex Optimization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-02-19

AUTHORS

Peter Ochs, Thomas Brox, Thomas Pock

ABSTRACT

In this paper, we present a forward–backward splitting algorithm with additional inertial term for solving a strongly convex optimization problem of a certain type. The strongly convex objective function is assumed to be a sum of a non-smooth convex and a smooth convex function. This additional knowledge is used for deriving a worst-case convergence rate for the proposed algorithm. It is proved to be an optimal algorithm with linear rate of convergence. For certain problems this linear rate of convergence is better than the provably optimal worst-case rate of convergence for smooth strongly convex functions. We demonstrate the efficiency of the proposed algorithm in numerical experiments and examples from image processing. More... »

PAGES

171-181

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10851-015-0565-0

DOI

http://dx.doi.org/10.1007/s10851-015-0565-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049874795


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