Abstract
The intrinsic volumes are measures of the content of a convex body. This paper applies probabilistic and information-theoretic methods to study the sequence of intrinsic volumes. The main result states that the intrinsic volume sequence concentrates sharply around a specific index, called the central intrinsic volume. Furthermore, among all convex bodies whose central intrinsic volume is fixed, an appropriately scaled cube has the intrinsic volume sequence with maximum entropy.
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References
K. Adiprasito, R. Sanyal, Whitney numbers of arrangements via measure concentration of intrinsic volumes (2016). http://arXiv.org/abs/1606.09412
R.J. Adler, J.E. Taylor, Random fields and geometry. Springer Monographs in Mathematics (Springer, New York, 2007)
D. Amelunxen, M. Lotz, M.B. McCoy, J.A. Tropp, Living on the edge: phase transitions in convex programs with random data. Inf. Inference 3(3), 224–294 (2014)
S. Artstein-Avidan, A. Giannopoulos, V.D. Milman, Asymptotic geometric analysis. Part I. Mathematical Surveys and Monographs, vol. 202 (American Mathematical Society, Providence, 2015)
K. Ball, Volume ratios and a reverse isoperimetric inequality. J. Lond. Math. Soc. 44(2), 351–359 (1991)
K. Ball, An elementary introduction to modern convex geometry, in Flavors of geometry. Mathematical Sciences Research Institute Publications, vol. 31 (Cambridge Univ. Press, Cambridge, 1997), pp. 1–58
S. Bobkov, M. Madiman, Concentration of the information in data with log-concave distributions. Ann. Probab. 39(4), 1528–1543 (2011)
H.J. Brascamp, E.H. Lieb, On extensions of the Brunn-Minkowski and Prékopa-Leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation. J. Funct. Anal. 22(4), 366–389 (1976)
P. Caputo, P. Dai Pra, G. Posta, Convex entropy decay via the Bochner-Bakry-Emery approach. Ann. Inst. Henri Poincaré Probab. Stat. 45(3), 734–753 (2009)
S. Chevet, Processus Gaussiens et volumes mixtes. Z. Wahrscheinlichkeitstheorie und Verw. Gebiete 36(1), 47–65 (1976)
H. Federer, Curvature measures. Trans. Am. Math. Soc. 93, 418–491 (1959)
M. Fradelizi, M. Madiman, L. Wang, Optimal concentration of information content for log-concave densities, in High dimensional probability VII. Progress in Probability, vol. 71 (Springer, Berlin, 2016), pp. 45–60
L. Goldstein, I. Nourdin, G. Peccati, Gaussian phase transitions and conic intrinsic volumes: steining the Steiner formula. Ann. Appl. Probab. 27(1), 1–47 (2017)
P.M. Gruber, Convex and discrete geometry. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathematical Sciences], vol. 336 (Springer, Berlin, 2007)
H. Hadwiger, Beweis eines Funktionalsatzes für konvexe Körper. Abh. Math. Sem. Univ. Hamburg 17, 69–76 (1951)
H. Hadwiger, Additive Funktionale k-dimensionaler Eikörper. I. Arch. Math. 3, 470–478 (1952)
H. Hadwiger, Vorlesungen über Inhalt, Oberfläche und Isoperimetrie (Springer, Berlin, 1957)
H. Hadwiger, Das Wills’sche funktional. Monatsh. Math. 79, 213–221 (1975)
O. Johnson, Log-concavity and the maximum entropy property of the Poisson distribution. Stoch. Process. Appl. 117(6), 791–802 (2007)
D.A. Klain, G.-C. Rota, Introduction to geometric probability. Lezioni Lincee. [Lincei Lectures] (Cambridge University Press, Cambridge, 1997)
M.B. McCoy, A geometric analysis of convex demixing (ProQuest LLC, Ann Arbor, 2013). Thesis (Ph.D.)–California Institute of Technology
M.B. McCoy, J.A. Tropp, From Steiner formulas for cones to concentration of intrinsic volumes. Discrete Comput. Geom. 51(4), 926–963 (2014)
M.B. McCoy, J.A. Tropp, Sharp recovery bounds for convex demixing, with applications. Found. Comput. Math. 14(3), 503–567 (2014)
M.B. McCoy, J.A. Tropp, The achievable performance of convex demixing. ACM Report 2017-02, California Institute of Technology (2017). Manuscript dated 28 Sep. 2013
P. McMullen, Non-linear angle-sum relations for polyhedral cones and polytopes. Math. Proc. Cambridge Philos. Soc. 78(2), 247–261 (1975)
P. McMullen, Inequalities between intrinsic volumes. Monatsh. Math. 111(1), 47–53 (1991)
V.H. Nguyen, Inégalités Fonctionelles et Convexité. Ph.D. Thesis, Université Pierrre et Marie Curie (Paris VI) (2013)
G. Paouris, P. Pivovarov, P. Valettas. On a quantitative reversal of Alexandrov’s inequality. Trans. Am. Math. Soc. 371(5), 3309–3324 (2019)
G. Pisier, The volume of convex bodies and Banach space geometry. Cambridge Tracts in Mathematics, vol. 94 (Cambridge University Press, Cambridge, 1989)
L.A. SantalĂł, Integral geometry and geometric probability. Cambridge Mathematical Library, 2nd edn. (Cambridge University Press, Cambridge, 2004). With a foreword by Mark Kac
A. Saumard, J.A. Wellner, Log-concavity and strong log-concavity: a review. Stat. Surv. 8, 45–114 (2014)
R. Schneider, Convex bodies: the Brunn-Minkowski theory. Encyclopedia of Mathematics and its Applications, vol. 151, expanded edn. (Cambridge University Press, Cambridge, 2014)
R. Schneider, W. Weil, Stochastic and integral geometry. Probability and its Applications (New York) (Springer, Berlin, 2008)
Y. Shenfeld, R. van Handel, Mixed volumes and the Bochner method. Proc. Amer. Math. Soc. 147, 5385–5402 (2019)
R.A. Vitale, The Wills functional and Gaussian processes. Ann. Probab. 24(4), 2172–2178 (1996)
L. Wang, Heat Capacity Bound, Energy Fluctuations and Convexity (ProQuest LLC, Ann Arbor, 2014). Thesis (Ph.D.)–Yale University
J.M. Wills, Zur Gitterpunktanzahl konvexer Mengen. Elem. Math. 28, 57–63 (1973)
H.S. Wilf, generatingfunctionology, 2nd edn. (Academic Press, Boston, 1994)
Y. Yu, On the maximum entropy properties of the binomial distribution. IEEE Trans. Inform. Theory 54(7), 3351–3353 (2008)
Acknowledgements
We are grateful to Emmanuel Milman for directing us to the literature on concentration of information. Dennis Amelunxen, Sergey Bobkov, and Michel Ledoux also gave feedback at an early stage of this project. Ramon Van Handel provided valuable comments and citations, including the fact that ULC sequences concentrate. We thank the anonymous referee for a careful reading and constructive remarks.
Parts of this research were completed at Luxembourg University and at the Institute for Mathematics and its Applications (IMA) at the University of Minnesota. Giovanni Peccati is supported by the internal research project STARS (R-AGR-0502-10) at Luxembourg University. Joel A. Tropp gratefully acknowledges support from ONR award N00014-11-1002 and the Gordon and Betty Moore Foundation.
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Lotz, M., McCoy, M.B., Nourdin, I., Peccati, G., Tropp, J.A. (2020). Concentration of the Intrinsic Volumes of a Convex Body. In: Klartag, B., Milman, E. (eds) Geometric Aspects of Functional Analysis. Lecture Notes in Mathematics, vol 2266. Springer, Cham. https://doi.org/10.1007/978-3-030-46762-3_6
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