An inertial proximal point method for difference of maximal monotone vector fields in Hadamard manifolds

João S. Andrade, Jurandir de O. Lopes and João Carlos de O. Souza
Additional contact information
João S. Andrade: Federal University of Piauí
Jurandir de O. Lopes: Federal University of Piauí
João Carlos de O. Souza: UFPI - Universidade Federal do Piauí, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique

Post-Print from HAL

Abstract: We propose an inertial proximal point method for variational inclusion involving difference of two maximal monotone vector fields in Hadamard manifolds. We prove that if the sequence generated by the method is bounded, then every cluster point is a solution of the non-monotone variational inclusion. Some sufficient conditions for boundedness and full convergence of the sequence are presented. The efficiency of the method is verified by numerical experiments comparing its performance with classical versions of the method for monotone and non-monotone problems.

Keywords: Variational inclusion; Proximal point method; Monotone vector fields; DC functions; Hadamard manifolds (search for similar items in EconPapers)
Date: 2023-04
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03866947
References: Add references at CitEc
Citations:

Published in Journal of Global Optimization, 2023, 85 (4), pp.941-968. ⟨10.1007/s10898-022-01240-1⟩

Downloads: (external link)
https://amu.hal.science/hal-03866947/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03866947

DOI: 10.1007/s10898-022-01240-1

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().