A New Method for Deriving Robust and Globalized Robust Solutions of Uncertain Linear Conic Optimization Problems Having General Convex Uncertainty Sets

B.L. Gorissen, A. Ben-Tal, J.P.C. Blanc, D. den Hertog

Research output: Working paperDiscussion paperOther research output

437 Downloads (Pure)

Abstract

Abstract: We propose a new way to derive tractable robust counterparts of a linear conic optimization problem by using the theory of Beck and Ben-Tal [2] on the duality between the robust (“pessimistic”) primal problem and its “optimistic” dual. First, we obtain a new convex reformulation of the dual problem of a robust linear conic program, and then show how to construct the primal robust solution from the dual optimal solution. Our result allows many new uncertainty regions to be considered. We give examples of tractable uncertainty regions that were previously intractable. The results are illustrated by solving a multi-item newsvendor problem. We also propose a new globalized robust counterpart that is more flexible, and is tractable for general convex uncertainty sets and any convex distance function.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages16
Volume2012-076
Publication statusPublished - 2012

Publication series

NameCentER Discussion Paper
Volume2012-076

Keywords

  • robust optimization
  • general convex uncertainty regions
  • linear conic optimization

Fingerprint

Dive into the research topics of 'A New Method for Deriving Robust and Globalized Robust Solutions of Uncertain Linear Conic Optimization Problems Having General Convex Uncertainty Sets'. Together they form a unique fingerprint.

Cite this