M
Mikkel Thorup
Researcher at University of Copenhagen
Publications - 306
Citations - 17294
Mikkel Thorup is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Time complexity & Hash function. The author has an hindex of 63, co-authored 297 publications receiving 16344 citations. Previous affiliations of Mikkel Thorup include Max Planck Society & University of Copenhagen Faculty of Science.
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Proceedings ArticleDOI
Sketching unaggregated data streams for subpopulation-size queries
TL;DR: This work design efficient streaming algorithms that summarize unaggregated streams and provide corresponding unbiased estimators for subpopulation sizes and performance of the best method, step sample-and-hold is close to that of summaries that can be obtained from pre-aggregation traffic.
Proceedings ArticleDOI
Optimal evolutionary tree comparison by sparse dynamic programming
Martin Farach,Mikkel Thorup +1 more
TL;DR: Sparsification of MAST shows that MAST is equivalent to Unary Weighted Bipartite Matching (UWBM) modulo an O(nc/sup /spl radic/(log n/) additive overhead) time algorithm for the special case of bounded degrees.
Journal ArticleDOI
Coloring 3-Colorable Graphs with Less than n1/5 Colors
TL;DR: This work presents a new combinatorial algorithm using Õ(n4/11) colors, which composes immediately with recent semi-definite programming approaches, and improves the best bound for the polynomial time algorithm for the coloring of 3-colorable graphs from O( n0.2072) colors by Chlamtac from FOCS’07 to O(n0.19996) colors.
Proceedings ArticleDOI
Simple Tabulation, Fast Expanders, Double Tabulation, and High Independence
TL;DR: Siegel [FOCS'89, SICOMP'04] has proved that with this space, if the hash function is evaluated in o( c) time, then the independence can only be o(c), so the evaluation time is best possible for Ω(c) independence.
Proceedings ArticleDOI
String hashing for linear probing
TL;DR: The authors' contribution is that for an expected constant number of linear probes, it is suffices that each key has O(1) expected collisions with the first hash function, as long as the second hash function is 5-universal.