Robust stochastic dominance and its application to risk-averse optimization

Darinka Dentcheva, Andrzej Ruszczyński

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

We introduce a new preference relation in the space of random variables, which we call robust stochastic dominance. We consider stochastic optimization problems where risk-aversion is expressed by a robust stochastic dominance constraint. These are composite semi-infinite optimization problems with constraints on compositions of measures of risk and utility functions. We develop necessary and sufficient conditions of optimality for such optimization problems in the convex case. In the nonconvex case, we derive necessary conditions of optimality under additional smoothness assumptions of some mappings involved in the problem.

Original languageEnglish
Pages (from-to)85-100
Number of pages16
JournalMathematical Programming
Volume123
Issue number1
DOIs
StatePublished - May 2010

Keywords

  • Risk constraints
  • Robust preferences
  • Semi-infinite optimization
  • Stochastic dominance constraints
  • Stochastic order

Fingerprint

Dive into the research topics of 'Robust stochastic dominance and its application to risk-averse optimization'. Together they form a unique fingerprint.

Cite this