Robust optimization in the presence of uncertainty: A generic approach
OPEN ACCESS
Author / Producer
Date
2018-06
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
94
Pages / Article No.
135 - 166
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Optimization; Uncertainty; Noise; Robustness; Instance similarity
Organisational unit
03340 - Widmayer, Peter (emeritus) / Widmayer, Peter (emeritus)
02803 - Collegium Helveticum / Collegium Helveticum
03659 - Buhmann, Joachim M. (emeritus) / Buhmann, Joachim M. (emeritus)
Notes
Funding
- Context Sensitive Information: Robust Optimization by Information Theoretic Regularization ()
Related publications and datasets
Is new version of: