Journal of Global Optimization

, Volume 60, Issue 4, pp 713–736

Primal and dual approximation algorithms for convex vector optimization problems

Authors

  • Andreas Löhne
    • Department of MathematicsMartin-Luther-Universität Halle-Wittenberg
    • Department of Operations Research and Financial EngineeringPrinceton University
    • Bendheim Center for FinancePrinceton University
  • Firdevs Ulus
    • Department of Operations Research and Financial EngineeringPrinceton University
Article

DOI: 10.1007/s10898-013-0136-0

Cite this article as:
Löhne, A., Rudloff, B. & Ulus, F. J Glob Optim (2014) 60: 713. doi:10.1007/s10898-013-0136-0

Abstract

Two approximation algorithms for solving convex vector optimization problems (CVOPs) are provided. Both algorithms solve the CVOP and its geometric dual problem simultaneously. The first algorithm is an extension of Benson’s outer approximation algorithm, and the second one is a dual variant of it. Both algorithms provide an inner as well as an outer approximation of the (upper and lower) images. Only one scalar convex program has to be solved in each iteration. We allow objective and constraint functions that are not necessarily differentiable, allow solid pointed polyhedral ordering cones, and relate the approximations to an appropriate \(\epsilon \)-solution concept. Numerical examples are provided.

Keywords

Vector optimizationMultiple objective optimizationConvex programmingDualityAlgorithmsOuter approximation

Copyright information

© Springer Science+Business Media New York 2014