Global descent methods for unconstrained global optimization

被引：0
作者：
Z. Y. Wu
D. Li
L. S. Zhang
机构：
[1] University of Ballarat,School of Information Technology and Mathematical Sciences
[2] The Chinese University of Hong Kong,Department of Systems Engineering and Engineering Management
[3] Shanghai University,Department of Mathematics
来源：
Journal of Global Optimization | 2011年 / 50卷
关键词：
Global descent method; Global optimization; Local search; Modified function approach; Non-convex optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We propose in this paper novel global descent methods for unconstrained global optimization problems to attain the global optimality by carrying out a series of local minimization. More specifically, the solution framework consists of a two-phase cycle of local minimization: the first phase implements local search of the original objective function, while the second phase assures a global descent of the original objective function in the steepest descent direction of a (quasi) global descent function. The key element of global descent methods is the construction of the (quasi) global descent functions which possess prominent features in guaranteeing a global descent.
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页码：379 / 396
页数：17
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