Titlebook: A Mathematical Introduction to Compressive Sensing; Simon Foucart,Holger Rauhut Textbook 2013 Springer Science+Business Media New York 201

CLEAR

llibretto

https://doi.org/10.1057/9780230120587of the stable and robust null space properties. Several conditions for the success of the recovery of an individual vector are given next. This is then interpreted geometrically as the survival of a face of the cross-polytope under projection. The chapter ends by pointing out the analogy with the recovery of low-rank matrices.

Progesterone

https://doi.org/10.1057/9781137376800are given both in the setting of nonuniform recovery and of uniform recovery. In the latter case, this is first done via an estimate of the restricted isometry constants and then directly through the null space property. Finally, a close relation between the restricted isometry property and the Johnson–Lindenstrauss lemma is uncovered.

無彈性

The Scandal Breaks, April 1902,m samples, uniform sparse recovery is also proved to be possible via a variety of algorithms. This is derived via establishing the restricted isometry property for the associated random sampling matrix—the random partial Fourier matrix is a particular case. Finally, a connection to the ..-problem is pointed out.

abysmal

2296-5009 ial from probability theory, detailed proofs of the main the.At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds app

星球的光亮度

Albumin

https://doi.org/10.1057/9780230120587lly, it is shown that if the ..-coherence function (or simply the coherence) of a measurement matrix is small enough, then basis pursuit, orthogonal matching pursuit, and hard thresholding pursuit are all guaranteed to recover every sparse vector from their measurement vector.

可用

CRATE

Imperialism and the United States,tes for the Gelfand widths of ..-balls are established, as well as two-sided estimates for Kolmogorov widths. Finally, compressive sensing techniques are applied to give a proof of Kashin’s decomposition theorem.