Buhmann, Joachim M.
- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
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. Show more
Journal / seriesJournal of Computer and System Sciences
Pages / Article No.
SubjectOptimization; Uncertainty; Noise; Robustness; Instance similarity
Organisational unitETH Zürich
03340 - Widmayer, Peter (emeritus) / Widmayer, Peter (emeritus)
02803 - Collegium Helveticum / Collegium Helveticum
03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
138117 - Context Sensitive Information: Robust Optimization by Information Theoretic Regularization (SNF)
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Is new version of: http://hdl.handle.net/20.500.11850/56319
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