Abstract.
Hesitant adaptive search is a stochastic optimisation procedure which accommodates hesitation, or pausing, at objective function values. It lies between pure adaptive search (which strictly improves at each iteration) and simulated annealing with constant temperature (which allows backtracking, or the acceptance of worse function values). In this paper we build on an earlier work and make two contributions; first, we link hesitant adaptive search to standard counting process theory, and second, we use this to derive the exact distribution of the number of iterations of hesitant adaptive search to termination.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Received: November 17, 1997 / Accepted: July 9, 1999¶Published online December 15, 2000
Rights and permissions
About this article
Cite this article
Wood, G., Zabinsky, Z. & Kristinsdottir, B. Hesitant adaptive search: the distribution of the number of iterations to convergence. Math. Program. 89, 479–486 (2001). https://doi.org/10.1007/PL00011410
Issue Date:
DOI: https://doi.org/10.1007/PL00011410