
arXiv: 1906.09129
In this article we use techniques of proof mining to analyse a result, due to Yonghong Yao and Muhammad Aslam Noor, concerning the strong convergence of a generalized proximal point algorithm which involves multiple parameters. Yao and Noor’s result ensures the strong convergence of the algorithm to the nearest projection point onto the set of zeros of the operator. Our quantitative analysis, guided by Fernando Ferreira and Paulo Oliva’s bounded functional interpretation, provides a primitive recursive bound on the metastability for the convergence of the algorithm, in the sense of Terence Tao. Furthermore, we obtain quantitative information on the asymptotic regularity of the iteration. The results of this paper are made possible by an arithmetization of the lim sup.
Convex programming, convex optimization, Applications of functional analysis in optimization, convex analysis, mathematical programming, economics, Mathematics - Logic, Functionals in proof theory, proximal point algorithm, metastability, 90C25, 47H09, 46N10, 03F10, 03F60, proof mining, Optimization and Control (math.OC), FOS: Mathematics, Contraction-type mappings, nonexpansive mappings, \(A\)-proper mappings, etc., Logic (math.LO), Mathematics - Optimization and Control, Constructive and recursive analysis
Convex programming, convex optimization, Applications of functional analysis in optimization, convex analysis, mathematical programming, economics, Mathematics - Logic, Functionals in proof theory, proximal point algorithm, metastability, 90C25, 47H09, 46N10, 03F10, 03F60, proof mining, Optimization and Control (math.OC), FOS: Mathematics, Contraction-type mappings, nonexpansive mappings, \(A\)-proper mappings, etc., Logic (math.LO), Mathematics - Optimization and Control, Constructive and recursive analysis
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