A Trust-Region Algorithm for Global Optimization

被引：0
|
作者：
Bernardetta Addis
Sven Leyffer
机构：
[1] Università di Firenze,Dipartimento Sistemi e Informatica
[2] Argonne National Laboratory,Mathematics and Computer Science Division
来源：
Computational Optimization and Applications | 2006年 / 35卷
关键词：
global optimization; smoothing; trust region;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We consider the global minimization of a bound-constrained function with a so-called funnel structure. We develop a two-phase procedure that uses sampling, local optimization, and Gaussian smoothing to construct a smooth model of the underlying funnel. The procedure is embedded in a trust-region framework that avoids the pitfalls of a fixed sampling radius. We present a numerical comparison to three popular methods and show that the new algorithm is robust and uses up to 20 times fewer local minimizations steps.
引用
收藏
页码：287 / 304
页数：17
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