Steepest Descent

Meza, Juan C.

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

关键词: Lawrence Berkeley Laboratory Optimization;    Minimization;    Gradient;    Cauchy;    Optimization;   

DOI  :  10.2172/983240
RP-ID  :  LBNL--3395E
RP-ID  :  AC02-05CH11231
RP-ID  :  983240

美国|英语

来源: UNT Digital Library

【 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 optimization methods. 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.