Backtracking Adaptive Search: Distribution of Number of Iterations to Convergence

G. R. Wood, D. W. Bulger, W. P. Baritompa and D. L. J. Alexander
Additional contact information
G. R. Wood: Macquarie University
D. W. Bulger: Macquarie University
W. P. Baritompa: University of Canterbury
D. L. J. Alexander: Massey University

Journal of Optimization Theory and Applications, 2006, vol. 128, issue 3, No 4, 547-562

Abstract: Abstract Backtracking adaptive search is a simplified stochastic optimiza-tion procedure which permits the acceptance of worsening objective function values. It generalizes the hesitant adaptive search, which in turn is a gener-alization of the pure adaptive search. In this paper, we use ideas from the theory of stochastic processes to determine the full distribution of the number of iterations to convergence for the backtracking adaptive search.

Keywords: Adaptive search; convergence rates; global optimization; point processes; simulated annealing (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-006-9040-9

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