Steepest descent

被引:193
作者
Meza, Juan C. [1 ]
机构
[1] Lawrence Berkeley Natl Lab, High Performance Comp Res, 1 Cyclotron Rd, Berkeley, CA 94720 USA
关键词
optimization; gradient; minimization; Cauchy;
D O I
10.1002/wics.117
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The steepest descent method has a rich history and is one of the simplest and best known methods for minimizing a function. While the method is not commonly used in practice due to its slow convergence rate, understanding the convergence properties of this method can lead to a better understanding of many of the more sophisticated optimizationmethods. Here, we give a short introduction and discuss some of the advantages and disadvantages of this method. Some recent results on modified versions of the steepest descent method are also discussed. (C) 2010 John Wiley & Sons, Inc.
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页码:719 / 722
页数:4
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