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How much is it worth to know the future in online conversion problems?

Authors
Journal
Discrete Applied Mathematics
0166-218X
Publisher
Elsevier
Volume
161
Identifiers
DOI: 10.1016/j.dam.2013.01.001
Keywords
  • Online Algorithms
  • Competitive Analysis
  • Empirical-Case Analysis
  • Worst-Case Analysis
  • One-Way Trading
  • Uni-Directional Algorithm
Disciplines
  • Computer Science

Abstract

Abstract We answer this question using the competitive ratio as an indicator for the quality of information about the future. Analytical results show that the better the information the better the worst-case competitive ratios. However, experimental analysis gives a slightly different view. We calculate the empirical-case competitive ratios of different variants of a threat-based online algorithm. The results are based on historical data of the German Dax-30 index. We compare our experimental empirical-case results to the analytical worst-case results given in the literature. We show that better information does not always lead to a better performance in real life applications. The empirical-case competitive ratio is not always better with better information, and some a-priori information is more valuable than other for practical settings.

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