On the geometry of polytopes generated by heavy-tailed random vectors

被引:4
|
作者
Guedon, Olivier [1 ]
Krahmer, Felix [2 ]
Kummerle, Christian [3 ]
Mendelson, Shahar [4 ,5 ]
Rauhut, Holger [6 ]
机构
[1] Univ Paris Est Creteil, UPEM, Univ Gustave Eiffel, LAMA,CNRS, F-77447 Marne La Vallee, France
[2] Tech Univ Munich, Dept Math, D-85748 Garching, Germany
[3] Johns Hopkins Univ, Dept Appl Math & Stat, Baltimore, MD USA
[4] Sorbonne Univ, LPSM, Paris, France
[5] Australian Natl Univ, Math Sci Inst, Canberra, ACT, Australia
[6] Rhein Westfal TH Aachen, Chair Math Informat Proc, D-52056 Aachen, Germany
基金
英国工程与自然科学研究理事会;
关键词
Random polytopes; random matrices; heavy tails; small ball probability; compressed sensing; l(1)-quotient property; CONVERGENCE; ROBUSTNESS; STABILITY; MATRICES; SUPREMA;
D O I
10.1142/S0219199721500565
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We study the geometry of centrally symmetric random polytopes, generated by N independent copies of a random vector X taking values in R-n. We show that under minimal assumptions on X, for N greater than or similar to n and with high probability, the polytope contains a deterministic set that is naturally associated with the random vector - namely, the polar of a certain floating body. This solves the long-standing question on whether such a random polytope contains a canonical body. Moreover, by identifying the floating bodies associated with various random vectors, we recover the estimates that were obtained previously, and thanks to the minimal assumptions on X, we derive estimates in cases that were out of reach, involving random polytopes generated by heavy-tailed random vectors (e.g., when X is q-stable or when X has an unconditional structure). Finally, the structural results are used for the study of a fundamental question in compressive sensing - noise blind sparse recovery.
引用
收藏
页数:31
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