Incremental Quasi-Subgradient Method for Minimizing Sum of Geodesic Quasi-Convex Functions on Riemannian Manifolds with Applications

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

To find the optimal solution of a sum of geodesic quasi-convex functions, we introduce a new path incremental quasi-subgradient method in the setting of a Riemannian manifold whose sectional curvature is nonnegative. To study the convergence analysis of the proposed algorithm, some auxiliary results related to geodesic quasi-convex functions and an existence result for a Greenberg-Pierskalla quasi-subgradient of the geodesic quasi-convex function in the setting of Riemannian manifolds are established. The convergence result of the proposed algorithm with the dynamic step size is presented in the case when the optimal solution is unknown. To demonstrate practical applicability, we show that the proposed method can be used to find a solution of the (geodesic) quasi-convex feasibility problems and the sum of ratio problems in the setting of Riemannian manifolds.

Original languageEnglish
Pages (from-to)1492-1521
Number of pages30
JournalNumerical Functional Analysis and Optimization
Volume42
Issue number13
DOIs
StatePublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.

Keywords

  • Greenberg-Pierskalla quasi-subdifferential
  • Quasi-convex optimization problems
  • Riemannian manifolds
  • subgradient method
  • sum-minimization problems

ASJC Scopus subject areas

  • Analysis
  • Signal Processing
  • Computer Science Applications
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Incremental Quasi-Subgradient Method for Minimizing Sum of Geodesic Quasi-Convex Functions on Riemannian Manifolds with Applications'. Together they form a unique fingerprint.

Cite this