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Electronic Colloquium on Computational Complexity

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All reports by Author Uri Zwick:

TR00-060 | 17th August 2000
Uri Zwick

All Pairs Shortest Paths using Bridging Sets and Rectangular Matrix Multiplication

We present two new algorithms for solving the {\em All
Pairs Shortest Paths\/} (APSP) problem for weighted directed
graphs. Both algorithms use fast matrix multiplication algorithms.

The first algorithm
solves the APSP problem for weighted directed graphs in which the edge
weights are integers of small absolute value in ... more >>>

TR97-040 | 17th September 1997
Dorit Dor, Shay Halperin, Uri Zwick

All Pairs Almost Shortest Paths

Let G=(V,E) be an unweighted undirected graph on n vertices. A simple
argument shows that computing all distances in G with an additive
one-sided error of at most 1 is as hard as Boolean matrix
multiplication. Building on recent work of Aingworth, Chekuri and
Motwani, we describe an \tilde{O}(min{n^{3/2}m^{1/2},n^{7/3}) time
more >>>

TR95-040 | 26th July 1995
Uri Zwick, Michael S. Paterson

The complexity of mean payoff games on graphs

We study the complexity of finding the values and optimal strategies of
MEAN PAYOFF GAMES on graphs, a family of perfect information games
introduced by Ehrenfeucht and Mycielski and considered by Gurvich,
Karzanov and Khachiyan. We describe a pseudo-polynomial time algorithm
for the solution of such games, the decision ... more >>>

TR95-031 | 25th June 1995
Dorit Dor, Uri Zwick

Selecting the median

Improving a long standing result of Sch\"{o}nhage, Paterson
and Pippenger we show that the MEDIAN of a set containing n
elements can always be found using at most 2.95n comparisons.

This is the full version of the paper. An extended abstract
version ... more >>>

TR94-009 | 12th December 1994
Noga Alon, Raphael Yuster, Uri Zwick


We describe a novel randomized method, the method of
{\em color-coding\/} for finding simple paths and cycles of a specified
length $k$, and other small subgraphs, within a given graph $G=(V,E)$.
The randomized algorithms obtained using this method can be
derandomized using families of {\em perfect hash functions\/}. ... more >>>

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