2019
DOI: 10.1090/proc/14568
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The 𝐿_{𝑝} Aleksandrov problem for origin-symmetric polytopes

Abstract: The L p Aleksandrov integral curvature and its corresponding characterization problemthe L p Aleksandrov problem-were recently introduced by Huang, Lutwak, Yang, and Zhang. The current work presents a solution to the L p Aleksandrov problem for origin-symmetric polytopes when −1 < p < 0.

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Cited by 13 publications
(6 citation statements)
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“…In this case, the solution to the Gauss Image problem is shown to be unique up to a dilation. The L p analogues of the Aleksandrov problem were considered by Huang, Lutwak, Yang and Zhang in [21], by Mui in [32], and by Zhao in [50]. The L p analogue of the Gauss Image Problem was considered in [46] by C. Wu, D. Wu, and Xiang.…”
Section: Introductionmentioning
confidence: 99%
“…In this case, the solution to the Gauss Image problem is shown to be unique up to a dilation. The L p analogues of the Aleksandrov problem were considered by Huang, Lutwak, Yang and Zhang in [21], by Mui in [32], and by Zhao in [50]. The L p analogue of the Gauss Image Problem was considered in [46] by C. Wu, D. Wu, and Xiang.…”
Section: Introductionmentioning
confidence: 99%
“…Different proofs of the Aleksandrov problem were given by Oliker [34] and Bertand [8]. The L p analogues of the Aleksandrov problem were considered by Huang, Lutwak, Yang and Zhang in [18], by Mui in [30], and by Zhao in [42].…”
Section: Introductionmentioning
confidence: 99%
“…When G = t q /n and ϕ(t) = t p for 0 = p ∈ R, the general dual Orlicz-Minkowski problem reduces to the L p dual Minkowski problem [50]; contributions to this problem can be seen in, e.g., [3,10,11,14,33,38,41,55]. By letting G(u, t) = log t for all (u, t) ∈ S n−1 × (0, ∞), V G (K) for K ∈ K n (o) reduces to the dual entropy of K; in this case one can get the (L p and Orlicz) Aleksandrov problems [1,20,31] (see also [42,68]). Lastly, the general dual Orlicz-Minkowski problem also extends the dual Orlicz-Minkowski problems [65,69] and the Minkowski problem for Gaussian measures [32].…”
Section: Introductionmentioning
confidence: 99%