On List Update with Locality of Reference
We present a comprehensive study of the list update problem with locality of reference. More specifically, we present a combined theoretical and experimental study in which the theoretically proven and experimentally observed performance guarantees of algorithms match or nearly match. In the first part of the paper we introduce a new model of locality of reference that is based on the natural concept of runs. Using this model we develop refined theoretical analyses of popular list update algorithms. The second part of the paper is devoted to an extensive experimental study in which we have tested the algorithms on traces from benchmark libraries. It shows that the theoretical and experimental bounds differ by just a few percent. Our new bounds are substantially lower than those provided by standard competitive analysis. Another result is that the elegant Move-To-Front strategy exhibits the best performance, which confirms that it is the method of choice in practice.
KeywordsCompetitive Ratio Online Algorithm Performance Ratio Average Relative Error Theoretical Bound
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