Abstract
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.
Original language | English |
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Pages (from-to) | 457-467 |
Number of pages | 11 |
Journal | Journal of the Operations Research Society of China |
Volume | 5 |
Issue number | 4 |
DOIs | |
State | Published - 1 Dec 2017 |
Bibliographical note
Funding Information:This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior of the Federal University of Rio de Janeiro (UFRJ), Brazil.
Publisher Copyright:
© 2016, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.
Keywords
- Abstract subdifferential
- Convergence rate
- Hadamard manifolds
- Nonsmooth optimization
- Proximal point method
- Quasiconvex function