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The log-Minkowski inequality of curvature entropy. (English) Zbl 1519.52004

In calculus, the logarithm sometimes behaves as a “formal zero power”. This also happens in convexity theory, allowing some results to be generalized: in particular, we have the log-Brunn-Minkowski inequality and logarithmic Minkowski inequality of Böröczky et al.
Logarithms are also important in the definition of entropy, originally in thermodynamics (since the 19th century) and then in information theory and statistics. The various definitions have in common a formula along the lines of \(-\int f(x) \log(f(x)) dx\) and an interpretation as a measure of information content.
The authors introduce a curvature entropy for \(K,L \in\mathcal{K}^n_0\), defined as \[E(K,L) := - \int_{S^{n-1}} \log\frac{H_{n-1}(L)}{H_{n-1}(K)} dV_k ,\] where \(H\) is the Gaussian curvature of the boundary. They prove that for \(C^2\) bodies in the plane, \[E(K,L)) \leq \frac{V(K)}{2} \log\frac{V(L)}{V(K)},\] with equality only when the bodies are homothetic. They apply this to show that plane origin-symmetric \(C^2\) strictly-convex bodies with the same cone-volume measure must be equal, and relate the inequality above to other inequalities in the literature.

MSC:

52A39 Mixed volumes and related topics in convex geometry
52A40 Inequalities and extremum problems involving convexity in convex geometry
Full Text: DOI

References:

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