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ESA
2006
Springer

Stochastic Shortest Paths Via Quasi-convex Maximization

14 years 1 months ago
Stochastic Shortest Paths Via Quasi-convex Maximization
Abstract. We consider the problem of finding shortest paths in a graph with independent randomly distributed edge lengths. Our goal is to maximize the probability that the path length does not exceed a given threshold value (deadline). We give a surprising exact n(log n) algorithm for the case of normally distributed edge lengths, which is based on quasi-convex maximization. We then prove average and smoothed polynomial bounds for this algorithm, which also translate to average and smoothed bounds for the parametric shortest path problem, and extend to a more general non-convex optimization setting. We also consider a number other edge length distributions, giving a range of exact and approximation schemes.
Evdokia Nikolova, Jonathan A. Kelner, Matthew Bran
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ESA
Authors Evdokia Nikolova, Jonathan A. Kelner, Matthew Brand, Michael Mitzenmacher
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