AVERAGE PERFORMANCE OF A CLASS OF ADAPTIVE ALGORITHMS FOR GLOBAL OPTIMIZATION
成果类型：
Article
署名作者：
Calvin, James M.
署名单位：
New Jersey Institute of Technology
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
发表日期：
1997
页码：
711-730
关键词：
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.