A Comparison of Performance Measures via Online Search

Joan Boyar, Kim Skak Larsen, Abyayananda Maiti

Research output: Contribution to journalJournal articleResearchpeer-review


Since the introduction of competitive analysis, a number of alternative measures for the quality of online algorithms have been proposed, but, with a few exceptions, these have generally been applied only to the online problem for which they were developed. Recently, a systematic study of performance measures for online algorithms was initiated [J. Boyar, S. Irani, K.S. Larsen, A comparison of performance measures for online algorithms, in: Eleventh International Algorithms and Data Structures Symposium, in: Lecture Notes in Computer Science, vol. 5664, Springer, 2009, pp. 119–130], first focusing on a simple server problem. We continue this work by studying a fundamentally different online problem, online search, and the Reservation Price Policies in particular. The purpose of this line of work is to learn more about the applicability of various performance measures in different situations and the properties that the different measures emphasize. We investigate the following analysis techniques: Competitive, Relative Worst Order, Bijective, Average, Relative Interval, Random Order, and Max/Max. In addition, we have established the first optimality proof for Relative Interval Analysis.
Original languageEnglish
JournalTheoretical Computer Science
Pages (from-to)2-13
Publication statusPublished - 2014


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