Abstract

We introduce and analyze a hybrid iterative algorithm by combining Korpelevich's extragradient method, the hybrid steepest-descent method, and the averaged mapping approach to the gradient-projection algorithm. It is proven that, under appropriate assumptions, the proposed algorithm converges strongly to a common element of the fixed point set of finitely many nonexpansive mappings, the solution set of a generalized mixed equilibrium problem (GMEP), the solution set of finitely many variational inclusions, and the solution set of a convex minimization problem (CMP), which is also a unique solution of a triple hierarchical variational inequality (THVI) in a real Hilbert space. In addition, we also consider the application of the proposed algorithm to solving a hierarchical variational inequality problem with constraints of the GMEP, the CMP, and finitely many variational inclusions.

Highlights

  • Let H be a real Hilbert space with inner product ⟨⋅, ⋅⟩ and norm ‖⋅‖, let C be a nonempty closed convex subset of H, and let PC be the metric projection of H onto C

  • We introduce and study the following triple hierarchical variational inequality (THVI) with constraints of generalized mixed equilibrium problem (GMEP) (3), convex minimization problem (CMP) (7), and finitely many variational inclusions

  • We prove the strong convergence of the proposed algorithm to a unique solution of THVI (16) under suitable conditions

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Summary

Introduction

Let H be a real Hilbert space with inner product ⟨⋅, ⋅⟩ and norm ‖⋅‖, let C be a nonempty closed convex subset of H, and let PC be the metric projection of H onto C. Assume that (i) K : H → R is strongly convex with a constant σ > 0 and its derivative K󸀠 is Lipschitz continuous with a constant ] > 0 such that the function x 󳨃→ ⟨y − x, K󸀠(x)⟩ is weakly upper semicontinuous for each y ∈ H; (ii) for each x ∈ H, there exist a bounded subset Dx ⊂ C and zx ∈ C such that, for any y ∉ Dx, Θ (y, zx)

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