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Advice Complexity and Barely Random Algorithms

Published online by Cambridge University Press:  24 June 2011

Dennis Komm  and
Richard Královič
Dennis Komm
Affiliation:
Department of Computer Science, ETH Zurich, Switzerland. [email protected]
Richard Královič
Affiliation:
Department of Computer Science, ETH Zurich, Switzerland. [email protected]
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Abstract

Recently, a new measurement – the advice complexity – was introduced for measuring the information content of online problems. The aim is to measure the bitwise information that online algorithms lack, causing them to perform worse than offline algorithms. Among a large number of problems, a well-known scheduling problem, job shop scheduling with unit length tasks, and the paging problem were analyzed within this model. We observe some connections between advice complexity and randomization. Our special focus goes to barely random algorithms, i.e., randomized algorithms that use only a constant number of random bits, regardless of the input size. We adapt the results on advice complexity to obtain efficient barely random algorithms for both the job shop scheduling and the paging problem. Furthermore, so far, it has not yet been investigated for job shop scheduling how good an online algorithm may perform when only using a very small (e.g., constant) number of advice bits. In this paper, we answer this question by giving both lower and upper bounds, and also improve the best known upper bound for optimal algorithms.

Keywords

Barely random algorithmsadvice complexityinformation contentonline problems
Type
Research Article
Information
RAIRO - Theoretical Informatics and Applications , Volume 45 , Issue 2 , April 2011 , pp. 249 - 267
Copyright
© EDP Sciences, 2011

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References

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