Abstract

The purpose of this paper is introduce several types of Levitin-Polyak well-posedness for bilevel vector equilibrium and optimization problems with equilibrium constraints. Base on criterion and characterizations for these types of Levitin-Polyak well-posedness we argue on diameters and Kuratowski’s, Hausdorff’s, or Istrǎtescus measures of noncompactness of approximate solution sets under suitable conditions, and we prove the Levitin-Polyak well-posedness for bilevel vector equilibrium and optimization problems with equilibrium constraints. Obtain a gap function for bilevel vector equilibrium problems with equilibrium constraints using the nonlinear scalarization function and consider relations between these types of LP well-posedness for bilevel vector optimization problems with equilibrium constraints and these types of Levitin-Polyak well-posedness for bilevel vector equilibrium problems with equilibrium constraints under suitable conditions; we prove the Levitin-Polyak well-posedness for bilevel equilibrium and optimization problems with equilibrium constraints.

Highlights

  • Well-posedness is one of most important topics for optimization theory and numerical methods of optimization problems, which guarantees that, for approximating solution sequences, there is a subsequence which converges to a solution

  • We focus on vector equilibria with equilibrium constraints and optimization with equilibrium constraints, as well as an abstract set constraint, and investigate criteria and characterizations for these types of LP well-posedness with a gap function for bilevel vector equilibrium problems with equilibrium constraints and optimization problems with equilibrium constraints

  • Problems (BVEPEC) is called type I LP well-posed if and only if (i) the solution set of (BVEPEC) is nonempty; (ii) for any type I, LP approximating sequence of (BVEPEC) has a subsequence converging to a solution

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Summary

Introduction

Well-posedness is one of most important topics for optimization theory and numerical methods of optimization problems, which guarantees that, for approximating solution sequences, there is a subsequence which converges to a solution. The constraints appear in this paper are solution sets of the following (parametric) vector quasi-equilibrium problem, for each z ∈ Z: (VQEPz) find x ∈ K1 (x, z) s.t. f (x, y, z) ∉ − int C (x) ,

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