A SELF ADAPTIVE INERTIAL ALGORITHM FOR SOLVING SPLIT VARIATIONAL INCLUSION AND FIXED POINT PROBLEMS WITH APPLICATIONS

Timilehin Opeyemi Alakoya, Lateef Olakunle Jolaoso, Oluwatosin Temitope Mewomoİ*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

We propose a general iterative scheme with inertial term and self-adaptive stepsize for approximating a common solution of Split Variational Inclusion Problem (SVIP) and Fixed Point Problem (FPP) for a quasi-nonexpansive mapping in real Hilbert spaces. We prove that our iterative scheme converges strongly to a common solution of SVIP and FPP for a quasi-nonexpansive mapping, which is also a solution of a certain optimization problem related to a strongly positive bounded linear operator. We apply our proposed algorithm to the problem of finding an equilibrium point with minimal cost of production for a model in industrial electricity production. Numerical results are presented to demonstrate the efficiency of our algorithm in comparison with some other existing algorithms in the literature.

Original languageEnglish
Pages (from-to)239-265
Number of pages27
JournalJournal of Industrial and Management Optimization
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

Keywords

  • Split variational inclusion problems
  • inertia
  • k-demicontractive mappings
  • quasi-nonexpansive mappings
  • strong convergence

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