辛布雷格曼散度。

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2024-12-16 DOI:10.3390/e26121101
Frank Nielsen
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引用次数: 0

摘要

我们提出了有限维辛向量空间中布雷格曼散度的一种推广,我们称之为辛布雷格曼散度。辛布雷格曼散度是由依赖于辛次微分概念的fenchell - young不等式的辛推广导出的。利用在辛形式下定义的辛芬切尔变换,得到辛芬切尔-杨不等式。由于对偶系统的对偶可以一般地建立辛形式,因此我们得到了对偶系统中由等效辛布雷格曼散度得到的布雷格曼散度的推广。特别地,当辛形式由内积导出时,我们证明了对应的辛Bregman散度等于复合内积的普通Bregman散度。讨论了辛散度在几何力学、信息几何和学习动力学中的潜在应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Symplectic Bregman Divergences.

We present a generalization of Bregman divergences in finite-dimensional symplectic vector spaces that we term symplectic Bregman divergences. Symplectic Bregman divergences are derived from a symplectic generalization of the Fenchel-Young inequality which relies on the notion of symplectic subdifferentials. The symplectic Fenchel-Young inequality is obtained using the symplectic Fenchel transform which is defined with respect to the symplectic form. Since symplectic forms can be built generically from pairings of dual systems, we obtain a generalization of Bregman divergences in dual systems obtained by equivalent symplectic Bregman divergences. In particular, when the symplectic form is derived from an inner product, we show that the corresponding symplectic Bregman divergences amount to ordinary Bregman divergences with respect to composite inner products. Some potential applications of symplectic divergences in geometric mechanics, information geometry, and learning dynamics in machine learning are touched upon.

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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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