On the Convergence Rate of an Inexact Proximal Point Algorithm for Quasiconvex Minimization on Hadamard Manifolds

被引：6
|
作者：
Baygorrea N. [1 ]
Papa Quiroz E.A. [1 ]
Maculan N. [1 ]
机构：
[1] Federal University of Rio de Janeiro, PESC-COPPE-UFRJ, PO Box 68511, Rio de Janeiro
来源：
Journal of the Operations Research Society of China | 2017年 / 5卷 / 04期
关键词：
Abstract subdifferential; Convergence rate; Hadamard manifolds; Nonsmooth optimization; Proximal point method; Quasiconvex function;
D O I：
10.1007/s40305-016-0129-z
中图分类号：
学科分类号：
摘要：
In this paper, we present an analysis about the rate of convergence of an inexact proximal point algorithm to solve minimization problems for quasiconvex objective functions on Hadamard manifolds. We prove that under natural assumptions the sequence generated by the algorithm converges linearly or superlinearly to a critical point of the problem. © 2016, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：457 / 467
页数：10
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