research-article

Relative Worst-order Analysis: A Survey

Authors:

Lene M. Favrholdt,

Kim S. LarsenAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 1

Article No.: 8, Pages 1 - 21

https://doi.org/10.1145/3425910

Published: 02 January 2021 Publication History

Abstract

The standard measure for the quality of online algorithms is the competitive ratio. This measure is generally applicable, and for some problems it works well, but for others it fails to distinguish between algorithms that have very different performance. Thus, ever since its introduction, researchers have worked on improving the measure, defining variants, or defining measures based on other concepts to improve on the situation. Relative worst-order analysis (RWOA) is one of the most thoroughly tested such proposals. With RWOA, many separations of algorithms not obtainable with competitive analysis have been found.

In RWOA, two algorithms are compared directly, rather than indirectly as is done in competitive analysis, where both algorithms are compared separately to an optimal offline algorithm. If, up to permutations of the request sequences, one algorithm is always at least as good and sometimes better than another, then the first algorithm is deemed the better algorithm by RWOA.

We survey the most important results obtained with this technique and compare it with other quality measures. The survey includes a quite complete set of references.

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Cited By

Berg MBoyar JFavrholdt LLarsen K(2023)Online Minimum Spanning Trees with Weight PredictionsAlgorithms and Data Structures10.1007/978-3-031-38906-1_10(136-148)Online publication date: 28-Jul-2023
https://doi.org/10.1007/978-3-031-38906-1_10
Levin A(2022)Comparing the costs of Any Fit algorithms for bin packingOperations Research Letters10.1016/j.orl.2022.09.00650:6(646-649)Online publication date: Nov-2022
https://doi.org/10.1016/j.orl.2022.09.006

Index Terms

Relative Worst-order Analysis: A Survey
1. Theory of computation
  1. Design and analysis of algorithms
    1. Online algorithms

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 1

January 2022

844 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3446641

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Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 02 January 2021

Accepted: 01 September 2020

Revised: 01 July 2020

Received: 01 December 2018

Published in CSUR Volume 54, Issue 1

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Cited By

Berg MBoyar JFavrholdt LLarsen K(2023)Online Minimum Spanning Trees with Weight PredictionsAlgorithms and Data Structures10.1007/978-3-031-38906-1_10(136-148)Online publication date: 28-Jul-2023
https://doi.org/10.1007/978-3-031-38906-1_10
Levin A(2022)Comparing the costs of Any Fit algorithms for bin packingOperations Research Letters10.1016/j.orl.2022.09.00650:6(646-649)Online publication date: Nov-2022
https://doi.org/10.1016/j.orl.2022.09.006

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