When are static and adjustable robust optimization problems with constraint-wise uncertainty equivalent?

A. Marandi, Dick den Hertog

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)
129 Downloads (Pure)


Adjustable robust optimization (ARO) generally produces better worst-case solutions than static robust optimization (RO). However, ARO is computationally more difficult than RO. In this paper, we provide conditions under which the worst-case objective values of ARO and RO problems are equal. We prove that when the uncertainty is constraint-wise, the problem is convex with respect to the adjustable variables and concave with respect to the uncertain parameters, the adjustable variables lie in a convex and compact set and the uncertainty set is convex and compact, then robust solutions are also optimal for the corresponding ARO problem. Furthermore, we prove that if some of the uncertain parameters are constraint-wise and the rest are not, then under a similar set of assumptions there is an optimal decision rule for the ARO problem that does not depend on the constraint-wise uncertain parameters. Also, we show for a class of problems that using affine decision rules that depend on all of the uncertain parameters yields the same optimal objective value as when the rules depend solely on the non-constraint-wise uncertain parameters. Finally, we illustrate the usefulness of these results by applying them to convex quadratic and conic quadratic problems.

Original languageEnglish
Pages (from-to)555-568
Number of pages14
JournalMathematical Programming
Issue number2
Publication statusPublished - 1 Aug 2018


  • Robust Optimization
  • Adjustable Robust Optimization
  • Constraint-wise Uncertainty
  • Hybrid Uncertainty
  • Hybrid uncertainty
  • Adjustable robust optimization
  • Robust optimization
  • Constraint-wise uncertainty


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