Robust optimization in the presence of uncertainty: A generic approach

J.M. Buhmann*, A.Y. Gronskiy, M. Mihalák, T. Pröger, R. Šrámek, P. Widmayer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


We propose a novel approach for optimization under uncertainty. Our approach does not assume any particular noise model behind the measurements, and only requires two typical instances. We first propose a measure of similarity of instances (with respect to a given objective). Based on this measure, we then choose a solution randomly among all solutions that are near-optimum for both instances. The exact notion of near-optimum is intertwined with the proposed similarity measure. Our similarity measure also allows us to derive formal statements about the expected quality of the computed solution. Furthermore, we apply our approach to various optimization problems. (C) 2017 The Authors. Published by Elsevier Inc.

Original languageEnglish
Pages (from-to)135-166
Number of pages32
JournalJournal of Computer and System Sciences
Publication statusPublished - Jun 2018


  • Optimization, Uncertainty, Noise, Robustness, Instance similarity
  • Uncertainty
  • Noise
  • Instance similarity
  • Robustness
  • Optimization

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