Amortized efficiency of list update and paging rules
Communications of the ACM
Amortized analyses of self-organizing sequential search heuristics
Communications of the ACM - Lecture notes in computer science Vol. 174
On the power of randomization in online algorithms
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Two results on the list update problem
Information Processing Letters
An optimal on-line algorithm for metrical task system
Journal of the ACM (JACM)
A lower bound for randomized list update algorithms
Information Processing Letters
Fast algorithms for finding randomized strategies in game trees
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
A combined BIT and TIMESTAMP algorithm for the list update problem
Information Processing Letters
Online computation and competitive analysis
Online computation and competitive analysis
Improved randomized on-line algorithms for the list update problem
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Self-Organizing Data Structures
Developments from a June 1996 seminar on Online algorithms: the state of the art
Optimal Projective Algorithms for the List Update Problem
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
Offline List Update is NP-Hard
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
Optimal lower bounds for projective list update algorithms
ACM Transactions on Algorithms (TALG)
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The optimal competitive ratio for a randomized online list update algorithm is known to be at least 1.5 and at most 1.6, but the remaining gap is not yet closed. We present a new lower bound of 1.50084 for the partial cost model. The construction is based on game trees with incomplete information, which seem to be generally useful for the competitive analysis of online algorithms. 2001 Elsevier Science B.V.