Programming pearls: algorithm design techniques
Communications of the ACM
Prediction, Learning, and Games
Prediction, Learning, and Games
Efficient Algorithms for k Maximum Sums
Algorithmica
Improved Algorithms for the K-Maximum Subarray Problem
The Computer Journal
Improved algorithmms for the k maximum-sums problems
Theoretical Computer Science
The Concept of Recoverable Robustness, Linear Programming Recovery, and Railway Applications
Robust and Online Large-Scale Optimization
On the complexity of the metric TSP under stability considerations
SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
On robust online scheduling algorithms
Journal of Scheduling
Context sensitive information: model validation by information theory
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Stability of networks in stretchable graphs
SIROCCO'09 Proceedings of the 16th international conference on Structural Information and Communication Complexity
| Hi-index | 0.00 |
We study optimization in the presence of uncertainty such as noise in measurements, and advocate a novel approach of tackling it. The main difference to any existing approach is that we do not assume any knowledge about the nature of the uncertainty (such as for instance a probability distribution). Instead, we are given several instances of the same optimization problem as input, and, assuming they are typical w.r.t. the uncertainty, we make use of it in order to compute a solution that is good for the sample instances as well as for future (unknown) typical instances. We demonstrate our approach for the case of two typical input instances. We first propose a measure of similarity of instances with respect to an objective. This concept allows us to assess whether instances are indeed typical. Based on this concept, we then choose a solution randomly among all solutions that are near-optimum for both instances. We show that the exact notion of near-optimum is intertwined with the proposed measure of similarity. Furthermore, we will show that our measure of similarity also allows us to derive formal statements about the expected quality of the computed solution: If the given instances are not similar, or are too noisy, our approach will detect this. We demonstrate for a few optimization problems and real world data that our approach works well not only in theory, but also in practice.