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.
|Number of pages||11|
|Journal||Journal of the Operations Research Society of China|
|State||Published - 1 Dec 2017|
Bibliographical noteFunding Information:
This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior of the Federal University of Rio de Janeiro (UFRJ), Brazil.
© 2016, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.
- Abstract subdifferential
- Convergence rate
- Hadamard manifolds
- Nonsmooth optimization
- Proximal point method
- Quasiconvex function