Primal and dual approximation algorithms for convex vector optimization problems Authors
First Online: 12 January 2014 Received: 30 August 2013 Accepted: 20 December 2013 DOI:
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 optimization Multiple objective optimization Convex programming Duality Algorithms Outer approximation
B. Rudloff: Research supported by NSF award DMS-1007938.
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