AVERAGE PERFORMANCE OF A CLASS OF ADAPTIVE ALGORITHMS FOR GLOBAL OPTIMIZATION

作     者：Calvin, James M. 

作者机构：New Jersey Inst Technol Dept Comp & Informat Sci Newark NJ 07102 USA 

出 版 物：《ANNALS OF APPLIED PROBABILITY》 (应用概率纪事)

年 卷 期：1997年第7卷第3期

页      面：711-730页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：NSF [DDM-9010770  DMI-9500173] 

主　　题：Brownian motion global optimization average complexity 

摘      要：We describe a class of adaptive algorithms for approximating the global minimum of a continuous function on the unit interval. The limiting distribution of the error is derived under the assumption of Wiener measure on the objective functions. For any delta 0, we construct an algorithm which has error converging to zero at rate n(-(1-delta)) in the number of function evaluations n. This convergence rate contrasts with the n(-1/2) rate of previously studied nonadaptive methods.