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Backtracking Adaptive Search: Distribution of Number of Iterations to Convergence

Author

Listed:
  • G. R. Wood

    (Macquarie University)

  • D. W. Bulger

    (Macquarie University)

  • W. P. Baritompa

    (University of Canterbury)

  • D. L. J. Alexander

    (Massey University)

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.

Suggested Citation

  • G. R. Wood & D. W. Bulger & W. P. Baritompa & D. L. J. Alexander, 2006. "Backtracking Adaptive Search: Distribution of Number of Iterations to Convergence," Journal of Optimization Theory and Applications, Springer, vol. 128(3), pages 547-562, March.
    • Handle: RePEc:spr:joptap:v:128:y:2006:i:3:d:10.1007_s10957-006-9040-9
    DOI: 10.1007/s10957-006-9040-9
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    References listed on IDEAS

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    1. M. Locatelli, 2001. "Convergence and first hitting time of simulated annealing algorithms for continuous global optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 54(2), pages 171-199, December.
    2. D. Bulger & W. P. Baritompa & G. R. Wood, 2003. "Implementing Pure Adaptive Search with Grover's Quantum Algorithm," Journal of Optimization Theory and Applications, Springer, vol. 116(3), pages 517-529, March.
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    Cited by:

    1. Zelda B. Zabinsky & David D. Linz, 2023. "Hesitant adaptive search with estimation and quantile adaptive search for global optimization with noise," Journal of Global Optimization, Springer, vol. 87(1), pages 31-55, September.

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