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Optimal and myopic search in a binary random vector

Part of: Distribution theory - Probability

Published online by Cambridge University Press:  14 July 2016

Avner Dor ,
Eitan Greenshtein  and
Ephraim Korach
Avner Dor*
Affiliation:
HaNegev College
Eitan Greenshtein*
Affiliation:
Technion-Israel Institute of Technology
Ephraim Korach*
Affiliation:
Ben Gurion University
*
∗ Postal address: HaNegev College, D.N. Hof Ashkelon, 79165, Israel.
∗∗ Postal address: Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Kiryat HaTechnion, Haifa 32000, Israel. E-mail address: eitang@ie.technion.ac.il
∗∗∗ Postal address: Dept. of Industrial Engineering and Management, Ben Gurion University, Beet-Sheva, Israel
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Abstract

Let X = (X 1, …, X n ) be a random binary vector, with a known joint distribution P. It is necessary to inspect the coordinates sequentially in order to determine if X i = 0 for every i, i = 1, …, n. We find bounds for the ratio of the expected number of coordinates inspected using optimal and greedy searching policies.

Keywords

Greedy algorithmnegative correlation

MSC classification

Primary: 60E15: Inequalities; stochastic orderings

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 35 , Issue 2 , June 1998 , pp. 463 - 472
Copyright
Copyright © Applied Probability Trust 1998 

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References

Aigner, M. (1988). Combinatorial Search. John Wiley and Sons.Google Scholar
Dor, A. (1997). The ‘next most likely’ search-algorithm on boolean vectors. J. Algorithms 27, 42–60.Google Scholar
Dor, A., and Greenshtein, E. Greedy and optimal search-algorithms for testing graph connectivity. Submitted to J. Algorithms.Google Scholar
Fitzmaurice, G.M., Laird, N.M., and Rotnitzky, G.A. (1993). Regression models for discrete longitudinal responses. Statistical Science 8, 284–309.Google Scholar
Ross, S.M. (1969). A problem in optimal search and stop. Operat. Res. 17, 984–992.Google Scholar
Stone, L.D. (1975). Theory of Optimal Search (Mathematics in Science and Engineering, Vol. 118). New York, Academic Press.Google Scholar