Stopping rules in k-adaptive global random search algorithms

Authors:
Anatoly Zhigljavsky;Emily Hamilton
Affiliations:
School of Mathematics, Cardiff University, Cardiff, UK CF24 4AG;School of Mathematics, Cardiff University, Cardiff, UK CF24 4AG
Venue:
Journal of Global Optimization
Year:
2010

Citing 2
Cited 0

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Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications)

Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications)

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Abstract

In this paper we develop a methodology for defining stopping rules in a general class of global random search algorithms that are based on the use of statistical procedures. To build these stopping rules we reach a compromise between the expected increase in precision of the statistical procedures and the expected waiting time for this increase in precision to occur.