On list update and work function algorithms
Theoretical Computer Science
Invited Lecture: Online Algorithms: A Study of Graph-Theoretic Concepts
WG '99 Proceedings of the 25th International Workshop on Graph-Theoretic Concepts in Computer Science
On List Update and Work Function Algorithms
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
DCC '00 Proceedings of the Conference on Data Compression
Dynamic optimality for skip lists and B-trees
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Splay trees, Davenport-Schinzel sequences, and the deque conjecture
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
An Application of Self-organizing Data Structures to Compression
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
List update with locality of reference
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
List factoring and relative worst order analysis
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
Lists on lists: a framework for self-organizing lists in environments with locality of reference
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Parameterized analysis of paging and list update algorithms
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
List update with probabilistic locality of reference
Information Processing Letters
A new perspective on list update: probabilistic locality and working set
WAOA'11 Proceedings of the 9th international conference on Approximation and Online Algorithms
Optimal lower bounds for projective list update algorithms
ACM Transactions on Algorithms (TALG)
| Hi-index | 0.00 |
The best randomized on-line algorithms known so far for the list update problem achieve a competitiveness of $\sqrt{3} \approx 1.73$. In this paper we present a new family of randomized on-line algorithms that beat this competitive ratio. Our improved algorithms are called TIMESTAMP algorithms and achieve a competitiveness of $\max\{2-p, 1+p(2-p)\}$, for any real number $p\in[0,1]$. Setting $p = (3-\sqrt{5})/2$, we obtain a $\phi$-competitive algorithm, where $\phi = (1+\sqrt{5})/2\approx 1.62$ is the golden ratio. TIMESTAMP algorithms coordinate the movements of items using some information on past requests. We can reduce the required information at the expense of increasing the competitive ratio. We present a very simple version of the TIMESTAMP algorithms that is \mbox{$1.68$-competitive}. The family of TIME\-STAMP algorithms also includes a new deterministic 2-competitive on-line algorithm that is different from the MOVE-TO-FRONT rule.