Global optimization by monotonic transformation

被引:11
作者
Toh, KA [1 ]
机构
[1] Labs Informat Technol, Singapore, Singapore
关键词
global optimization; unconstrained minimization; convex functions; algorithms; nonlinear programming;
D O I
10.1023/A:1019976724755
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper addresses the problem of global optimization by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporate transformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for several neural network learning problems, which possess much larger parameter spaces.
引用
收藏
页码:77 / 99
页数:23
相关论文
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