Casado, Leocadio Gonzalez

Csendes, Tibor

García, I.

Martínez, J. A.

Casado Leocadio Gonzalez

Martínez J. A.

García I.

Csendes Tibor

University of Szeged

Adaptive Multisection in Derivative Free Interval Methods forGlobal Optimization2L.G. Casado, J.A. Martínez, I. García, and T. CsendesA new adaptive multisection technique in interval methods for global optimization is investigated,and numerical tests demonstrate that the efficiency of the underlying global optimization method canbe improved substantially. The heuristic rule is based on experiences that suggest the subdivisionof the current subinterval into a larger number of pieces only if it is located in the neighbourhoodof a minimizer point. An estimator of the proximity of a subinterval to the region of attraction to aminimizer point is utilized.This paper investigates adaptive multisection variants of a branch-and-bound algorithm [3, 4] forsolving the box constrained global optimization problem [5]:    (1)where the 	-dimensional interval     is the search region, and            is theobjective function. The global minimum value of  is denoted by  , and the set of global minimizerpoints of  on  by  . That is,              The paper discussed a new adaptive decision rule for multisection algorithms in interval globaloptimization. According to the theoretical results, the suggested algorithmic variant can save in thebest case up to one half of the inclusion function evaluations, while in the worst case, it may requirean arbitrary large times more function evaluations than standard bisection. The underlying  	 [1, 2]algorithm parameter is not necessarily reliable: in extreme cases the sequence of actual subintervalscan be distracted for an arbitrary long time, and also the dynamic multisection rule can suggestinefficient subdivision types.An extensive numerical study was made that was based on the exact, a priori known global min-imum value, on an approximation of it, and on the easily available upper bound 	 of it. These newadaptive multisection methods improved the number of interval (by about one third) and real func-tion evaluations (by around two third), and therefore also the execution time substantially (by upto a half). The memory complexity proved also to be smaller when larger accuracy was requested(by up to one half). The new adaptive multisection strategies seem to be indispensable, and canimprove both the computational and the memory complexity substantially. The introduced adaptivemultisection technique is appropriate for both differentiable and non-differentiable functions. Nev-ertheless, for differentiable functions our proposal could be improved further by applying it togetherwith sophisticated accelerating devices and other algorithmic changes.References[1] Casado, L. G., García, I.: New load balancing criterion for parallel interval global optimiza-tion algorithm. Proceedings of 16th IASTED International Conference. Garmish-Partenkircen,Germany, 321 – 323 (1998).[2] Casado, L. G., García, I., Csendes, T.: A Heuristic Rejection Criterion in Interval GlobalOptimization Algorithms. Submitted for publication. Available at http://www.inf.u-szeged.hu/csendes/publ.html.[3] Kearfott, R. B.: Rigorous global search: continuous problems. Dordrecht: Kluwer 1996.[4] Ratschek, H., Rokne, J.: New Computer Methods for Global Optimization. Chichester: EllisHorwood 1988.[5] Törn, A., Žilinskas, A.: Global Optimization. Berlin: Springer 1987.2This work was supported by the Ministry of Education of Spain (CICYT TIC99-0361, and by the Grants FKFP 0739/97,OTKA T 016413 and T 017241.19

Adaptive multisection in derivative free interval methods for global optimization : [abstract]

http://acta.bibl.u-szeged.hu/59578/1/cscs_2000_019.pdf

Adaptive multisection in derivative free interval methods for global optimization

Adaptive multisection in derivative free interval methods for global optimization

Abstract

Similar works

Full text

Available Versions

University of Szeged