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Updated: Jan 7, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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镜子下降和指数梯度算法使用痕迹形式度

Andrzej Cichocki1,2,3,4, Toshihisa Tanaka3, Frank Nielsen5

  • 1Systems Research Institute of Polish Academy of Science, Newelska 6, 01-447 Warsaw, Poland.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
概括

这项研究提出了新的镜像下降 (MD) 和使用通用的通用指数梯度 (GEG) 算法. 这些方法通过适应复杂的几何形状,提供了更好的收性和稳定性.

关键词:
(q,κ) - - 代数中的一个.布雷格曼分歧是什么意思里姆尼安优化的优化.变形的对数是对数的变形.一般化的指数梯度渐变.信息几何学信息几何学镜子下降的下降自然梯度的自然梯度.

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