Random polytopes obtained by matrices with heavy-tailed entries

被引:4
|
作者
Guedon, O. [1 ]
Litvak, A. E. [2 ]
Tatarko, K. [2 ]
机构
[1] Univ Paris Est Marne la Vallee, Lab Anal Math Appl, 5 Blvd Descartes, F-77454 Marne La Vallee 2, France
[2] Univ Alberta, Dept Math & Stat Sci, Edmonton, AB T6G 2G1, Canada
关键词
Random polytopes; random matrices; heavy tails; smallest singular number; small ball probability; compressed sensing; l(1)-quotient property; SMALLEST SINGULAR-VALUE; GEOMETRY; SPACES; INVERTIBILITY; OPERATORS; NUMBERS; VERSION; VALUES;
D O I
10.1142/S0219199719500275
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Let Gamma be an N x n random matrix with independent entries and such that in each row entries are i.i.d. Assume also that the entries are symmetric, have unit variances, and satisfy a small ball probabilistic estimate uniformly. We investigate properties of the corresponding random polytope Gamma* B-1(N) in R-n (the absolute convex hull of rows of Gamma). In particular, we show that Gamma* B-1(N) superset of b(-1) (B-infinity(n) boolean AND root ln(N/n) B-2(n)), where b depends only on parameters in small ball inequality. This extends results of [A. E. Litvak, A. Pajor, M. Rudelson and N. Tomczak-Jaegermann, Smallest singular value of random matrices and geometry of random polytopes, Adv. Math. 195 (2005) 491-523] and recent results of [F. Krahmer, C. Kummerle and H. Rauhut, A quotient property for matrices with heavy-tailed entries and its application to noise-blind compressed sensing, preprint (2018); arXiv:1806.04261]. This inclusion is equivalent to socalled l(1)-quotient property and plays an important role in compressed sensing (see [F. Krahmer, C. Kummerle and H. Rauhut, A quotient property for matrices with heavy-tailed entries and its application to noise-blind compressed sensing, preprint (2018); arXiv:1806.04261] and references therein).
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页数:28
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