从按比例总最小二乘法的角度看 Barzilai-Borwein 步长族

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-01-18 DOI:10.1007/s10589-023-00546-4

Shiru Li, Tao Zhang, Yong Xia

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引用次数: 0

摘要

Barzilai-Borwein（BB）步长在解决无约束优化问题的实际梯度方法中发挥着重要作用。观察到两种著名的 BB 步长分别对应于普通最小二乘法和数据最小二乘法，受此启发，我们从比例总最小二乘法的角度引入了一种新的 BB 步长系列。数值实验证明，在新系列中精心选择 BB 步长可以获得很高的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

A family of Barzilai-Borwein steplengths from the viewpoint of scaled total least squares

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A family of Barzilai-Borwein steplengths from the viewpoint of scaled total least squares

The Barzilai-Borwein (BB) steplengths play great roles in practical gradient methods for solving unconstrained optimization problems. Motivated by the observation that the two well-known BB steplengths correspond to the ordinary and the data least squares, respectively, we introduce a novel family of BB steplengths from the viewpoint of scaled total least squares. Numerical experiments demonstrate that high performance can be received by a carefully-selected BB steplength in the new family.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.