Estimates of the Duality Gap of Non-Convex Optimization Problems.

Abstract

The difference between the optimal values of an optimization problem and its dual is called 'the duality gap'. Under convenient assumptions (the so-called constraint qualification assumptions), it is known that the length of the duality gap is equal to zero when the functions and the constraints are convex. The aim of this paper is prove estimates of the duality gap in terms of a convenient measure of the 'lack of convexity' of the functions involved in the optimization problem.

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1975
Accession Number
ADA011014

Entities

People

  • Ivar Ekeland
  • Jean-pierre Aubin

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Optimization
  • Qualifications

Fields of Study

  • Mathematics

Readers

  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms