TY - JOUR
T1 - On the geometry of polytopes generated by heavy-tailed random vectors
AU - Guédon, Olivier
AU - Krahmer, Felix
AU - Kümmerle, Christian
AU - Mendelson, Shahar
AU - Rauhut, Holger
N1 - Publisher Copyright:
© 2022 World Scientific Publishing Company.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - We study the geometry of centrally symmetric random polytopes, generated by N independent copies of a random vector X taking values in ℝn. We show that under minimal assumptions on X, for N ≲ 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.
AB - We study the geometry of centrally symmetric random polytopes, generated by N independent copies of a random vector X taking values in ℝn. We show that under minimal assumptions on X, for N ≲ 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.
KW - Random polytopes
KW - compressed sensing
KW - heavy tails
KW - random matrices
KW - small ball probability
KW - ℓ1 -quotient property
UR - http://www.scopus.com/inward/record.url?scp=85107609015&partnerID=8YFLogxK
U2 - 10.1142/S0219199721500565
DO - 10.1142/S0219199721500565
M3 - Article
AN - SCOPUS:85107609015
SN - 0219-1997
VL - 24
JO - Communications in Contemporary Mathematics
JF - Communications in Contemporary Mathematics
IS - 3
M1 - 2150056
ER -