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An average-case analysis for a continuous random search algorithm

Published online by Cambridge University Press:  01 July 2016

Dietmar Pfeifer*
Affiliation:
Technical University Aachen
*
Postal address: Institut für Statistik und Wirtschaftsmathematik, Rheinisch-Westfälische Technische Hochschule, Wüllnerstrasse 3, D-5100 Aachen, W. Germany.
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Abstract

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We give an upper bound for the average complexity (i.e. the expected number of steps until termination) for a continuous random search algorithm using results from renewal theory. It is thus possible to show that for a predefined accuracy ε, the average complexity of the algorithm is O(–log ε) for ε → 0 which is optimal up to a constant factor.

Type
Letters to the Editor
Copyright
Copyright © Applied Probability Trust 1985 

References

Barth, G. (1983) Analyzing algorithms by Markov chains. In 7th Symposium on Operations Research, St. Gallen, Switzerland, 19-21 August 1982. Methods of Operations Research 45, 405418.Google Scholar
Jensen, U. (1984) Some remarks on the renewal function of the uniform distribution. Adv. Appl. Prob. 16, 214215.Google Scholar
Knuth, D. E. (1973) The Art of Computer Programming, Vol. 3: Sorting and Searching. Addison-Wesley, Reading, Mass. Google Scholar
Ross, S. M. (1983) Stochastic Processes. Wiley, New York.Google Scholar
Russell, K. G. (1983) On the number of uniform random variables that must be added to exceed a given level. J. Appl. Prob. 20, 172177.Google Scholar