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arXiv 1406.2137math.FAcs.NAmath.NA
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可扩展性的度量

Measures of scalability

University of Maryland(马里兰大学);Technische Universität Berlin(柏林工业大学);University of British Columbia(不列颠哥伦比亚大学)

Xuemei Chen, Gitta Kutyniok, Kasso A. Okoudjou, Friedrich Philipp, Rongrong Wang

AI总结:

本文提出三种度量框架接近可扩展性的方法,包括距离可扩展框架集的度量、二次函数优化度量及最小体积椭球体体积度量,并推导了随机框架可扩展的概率界和必要充分条件。

摘要:

可扩展框架是指其向量可重新缩放以形成紧框架。然而,若框架不可扩展,则需采用近似方法。本文引入三种新的量化度量,用于衡量有限维实欧几里得空间中框架的可扩展性接近程度。除了自然度量(框架到可扩展框架集的距离),另一种度量通过优化二次泛函获得,第三种则由对称化框架的最小体积椭球体的体积给出。在证明这些度量在某种意义上等价后,本文建立了随机选取框架可扩展的概率界。在此过程中,还推导了框架可扩展的新的必要且充分条件。

英文摘要:

Scalable frames are frames with the property that the frame vectors can be rescaled resulting in tight frames. However, if a frame is not scalable, one has to aim for an approximate procedure. For this, in this paper we introduce three novel quantitative measures of the closeness to scalability for frames in finite dimensional real Euclidean spaces. Besides the natural measure of scalability given by the distance of a frame to the set of scalable frames, another measure is obtained by optimizing a quadratic functional, while the third is given by the volume of the ellipsoid of minimal volume containing the symmetrized frame. After proving that these measures are equivalent in a certain sense, we establish bounds on the probability of a randomly selected frame to be scalable. In the process, we also derive new necessary and sufficient conditions for a frame to be scalable.

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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