TY - JOUR
T1 - On the Convergence Rate of an Inexact Proximal Point Algorithm for Quasiconvex Minimization on Hadamard Manifolds
AU - Baygorrea, Nancy
AU - Papa Quiroz, Erik Alex
AU - Maculan, Nelson
N1 - Publisher Copyright:
© 2016, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag Berlin Heidelberg.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - Abstract subdifferential
KW - Convergence rate
KW - Hadamard manifolds
KW - Nonsmooth optimization
KW - Proximal point method
KW - Quasiconvex function
UR - http://www.scopus.com/inward/record.url?scp=85037606159&partnerID=8YFLogxK
U2 - 10.1007/s40305-016-0129-z
DO - 10.1007/s40305-016-0129-z
M3 - Article
AN - SCOPUS:85037606159
SN - 2194-668X
VL - 5
SP - 457
EP - 467
JO - Journal of the Operations Research Society of China
JF - Journal of the Operations Research Society of China
IS - 4
ER -