TY - JOUR
T1 - Inexact Proximal Point Methods 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 - 2016/12/1
Y1 - 2016/12/1
N2 - In this paper we present two inexact proximal point algorithms to solve minimization problems for quasiconvex objective functions on Hadamard manifolds. We prove that under natural assumptions the sequence generated by the algorithms are well defined and converge to critical points of the problem. We also present an application of the method to demand theory in economy.
AB - In this paper we present two inexact proximal point algorithms to solve minimization problems for quasiconvex objective functions on Hadamard manifolds. We prove that under natural assumptions the sequence generated by the algorithms are well defined and converge to critical points of the problem. We also present an application of the method to demand theory in economy.
KW - Abstract subdifferential
KW - Hadamard manifolds
KW - Nonsmooth optimization
KW - Proximal point method
KW - Quasiconvex function
UR - http://www.scopus.com/inward/record.url?scp=84995595732&partnerID=8YFLogxK
U2 - 10.1007/s40305-016-0133-3
DO - 10.1007/s40305-016-0133-3
M3 - Article
AN - SCOPUS:84995595732
SN - 2194-668X
VL - 4
SP - 397
EP - 424
JO - Journal of the Operations Research Society of China
JF - Journal of the Operations Research Society of China
IS - 4
ER -