Susanne Albers and Michael Mitzenmacher, Average case analysis of list update algorithms, with applicaions to data compression, Proceedings of the 23rd International Colloquium on Automata, Languages and Programming, Springer Lecture Notes in Computer Science, Volume 1099, 1996, pp514-525 Home/Search Document Not in Database Summary Related Articles Check |
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Can Entropy Characterize Performance of Online Algorithms? - Pandurangan, Upfal (2001) (2 citations) (Correct)
....has been compared to the performance of a static o ine algorithm SOPT that initially arranges the list in decreasing order of request probabilities and never reorders them thereafter. It was shown in [15] that MTF(D) 2 SOPT(D) where D is the distribution of the source. Albers et.al [1] analyze the performance of the TIMESTAMP algorithm on a discrete memoryless source with distribution D and proved that for any distribution D, TIMESTAMP(D) 1:34 SOPT(D) and with high probability, TIMESTAMP(D) 1:5 OPT(D) The actual work done by the MTF algorithm was studied when the ....
.... distribution D and proved that for any distribution D, TIMESTAMP(D) 1:34 SOPT(D) and with high probability, TIMESTAMP(D) 1:5 OPT(D) The actual work done by the MTF algorithm was studied when the request sequence is generated by a discrete memoryless source with probability distribution D[1, 4, 15]. For online caching (or demand paging) the well known LRU (Least Recently Used) has a competitive ratio of k [24] where k is the cache size, while the randomized MARKER algorithm is 2 log k competitive [10] Franaszek and Wagner [13] studied a model in which every request is drawn from a ....
[Article contains additional citation context not shown here]
S. Albers and M. Mitzenmacher, Average Case Analysis of List Update Algorithms, with Applications to Data Compression, Algorithmica, 21, 1998, 312-329.
Can Entropy Characterize Performance of Online Algorithms? - Pandurangan, Upfal (2001) (2 citations) (Correct)
....has been compared to the performance of a static o ine algorithm SOPT that initially arranges the list in decreasing order of request probabilities and never reorders them thereafter. It was shown in [15] that MTF(D) 2 SOPT(D) where D is the distribution of the source. Albers et.al [1] analyze the performance of the TIMESTAMP algorithm on a discrete memoryless source with 1 An online algorithm ALG has a competitive ratio of c if there is a constant such that for all nite input sequences I , ALG(I) c OPT(I) where OPT is the optimal o ine algorithm. 1 distribution D ....
.... 1 distribution D and proved that for any distribution D, TIMESTAMP(D) 1:34 SOPT(D) and with high probability, TIMESTAMP(D) 1:5 OPT(D) The actual work done by the MTF algorithm was studied when the request sequence is generated by a discrete memoryless source with probability distribution D[1, 4, 15]. For online caching (or demand paging) the well known LRU (Least Recently Used) has a competitive ratio of k [25] where k is the cache size, while the randomized MARKER algorithm is 2 log k competitive [10] Franaszek and Wagner [13] studied a model in which every request is drawn from a ....
[Article contains additional citation context not shown here]
S. Albers and M. Mitzenmacher, Average Case Analysis of List Update Algorithms, with Applications to Data Compression, Algorithmica, 21, 1998, 312-329. 13
Improvements to Burrows-Wheeler Compression Algorithm - Deorowicz (2000) (Correct)
....speci c structure of sequence x bwt causes other algorithms solving this problem, e.g. Translate and Frequency Count, not to produce good results. Lately Albers [18] suggested a new algorithm, called Time Stamp, for solving the List Update Problem. The results obtained by Albers and Mitzenmacher [19] show that replacing the MTF with it in the BWCA results in a small improvement of the compression ratio only for some sequences, whilst for most cases the results are worse. 3.4 Zero Run Transform Zero is a dominant symbol of sequence x mtf . For some sequences x, taken from the Calgary ....
Albers S, Mitzenmacher M. Average case analysis of list update algorithms, with applications to data compression. Algorithmica 1998; 21(3):312-329. S. DEOROWICZ | IMPROVEMENTS TO BWCA 20
Algorithms for the List Update Problem - In Partial Ful (1997) (Correct)
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Susanne Albers and Michael Mitzenmacher, Average case analysis of list update algorithms, with applicaions to data compression, Proceedings of the 23rd International Colloquium on Automata, Languages and Programming, Springer Lecture Notes in Computer Science, Volume 1099, 1996, pp514-525
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