TheoremBase

Alexandrov's Theorem for Semiconvex Functions on an Open Convex Set

A semiconvex function with constant μ\mu on an open convex subset of Euclidean space is twice differentiable outside a null set, and wherever it is twice differentiable its Hessian is bounded below by −μIn-\mu I_n.

Statement

We work in the setting of Product Measures and Lebesgue Measure on ℝⁿ: Standing Notation and Background, whose notation is fixed for every dimension and is used here with a natural number nn satisfying 1≤n1\le n: the real numbers and finite index sets, the Euclidean norm ∥ ⋅ ∥\lVert\,\cdot\,\rVert, dot product, distance dEd_{E}, notion of openness and closed balls Bˉ(x,r)\bar{B}(x,r), the Lebesgue measure λn\lambda_{n}, the notion of a null subset, the constant σn\sigma_{n} with σn2=n\sigma_{n}^{2}=n, and the conventions on Lipschitz maps are as fixed there. Write S(n)\mathcal{S}(n) for the set of symmetric real n×nn\times n matrices, InI_{n} for the identity matrix, λA\lambda A for the scalar multiple of a real matrix, and ⪯\preceq for the positive semidefinite ordering on S(n)\mathcal{S}(n); the matrix (−μ)In(-\mu)I_{n} below belongs to S(n)\mathcal{S}(n) because all of its off-diagonal entries vanish.

Let U⊆RnU\subseteq\mathbb{R}^{n} be open and convex, let μ∈R\mu\in\mathbb{R} with 0≤μ0\le\mu, and let f:U→Rf:U\to\mathbb{R} be semiconvex on UU with constant μ\mu. Twice differentiability at a point and the Hessian D2f(y)D^{2}f(y) are as fixed there. Then the following hold.

1. (Twice differentiability almost everywhere) The set of those y∈Uy\in U at which ff is not twice differentiable is null.

2. (Lower bound on the Hessian) At every y∈Uy\in U at which ff is twice differentiable one has

(−μ)In⪯D2f(y).(-\mu)I_{n}\preceq D^{2}f(y).

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