arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2009.09907math.NAcs.NA
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最优稳定非线性逼近

Optimal Stable Nonlinear Approximation

Albert Cohen, Ronald DeVore, Guergana Petrova, Przemyslaw Wojtaszczyk

摘要:

虽然众所周知,近似的非线性方法往往可以执行显着优于线性方法,仍然有问题如何衡量这种方法可能的最佳性能。本文研究了非线性逼近方法,它们与数值实现兼容,因为它们需要数值稳定。本文介绍了一种用稳定流形方法逼近巴拿赫空间$X$中的模型类$K$的最优性能度量,称为{ \em稳定流形宽度}。这些稳定的流形宽度和$K$的熵之间的基本不等式的建立。讨论了在深度学习和压缩感知的设置中要求稳定性的影响。

英文摘要:

While it is well known that nonlinear methods of approximation can often perform dramatically better than linear methods, there are still questions on how to measure the optimal performance possible for such methods. This paper studies nonlinear methods of approximation that are compatible with numerical implementation in that they are required to be numerically stable. A measure of optimal performance, called {\em stable manifold widths}, for approximating a model class $K$ in a Banach space $X$ by stable manifold methods is introduced. Fundamental inequalities between these stable manifold widths and the entropy of $K$ are established. The effects of requiring stability in the settings of deep learning and compressed sensing are discussed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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