Amortized efficiency of list update and paging rules
Communications of the ACM
Competitive solutions for online financial problems
ACM Computing Surveys (CSUR)
Online computation and competitive analysis
Online computation and competitive analysis
Best-fit bin-packing with random order
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Optimal Buy-and-Hold Strategies for Financial Markets with Bounded Daily Returns
SIAM Journal on Computing
The relative worst order ratio for online algorithms
ACM Transactions on Algorithms (TALG)
The relative worst-order ratio applied to paging
Journal of Computer and System Sciences
On the separation and equivalence of paging strategies
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Note: Random-order bin packing
Discrete Applied Mathematics
On the relative dominance of paging algorithms
Theoretical Computer Science
A Comparison of Performance Measures for Online Algorithms
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
The frequent items problem in online streaming under various performance measures
FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
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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 [Boyar, Irani, Larsen: WADS 2009], 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, and Random Order. In addition, we have established the first optimality proof for Relative Interval Analysis.