TheoremBase

Extending a Convex Function from a Closed Ball to All of Rn\mathbb{R}^n

The supremum of the affine minorants furnished by the subgradients at points of a closed ball is a convex Lipschitz function on all of Euclidean space that agrees with the given convex function on that ball.

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 least upper bounds, the Euclidean norm ∥ ⋅ ∥\lVert\,\cdot\,\rVert, dot product, distance dEd_{E}, notion of openness and closed balls Bˉ(x,r)\bar{B}(x,r), and the convention that Lipschitz maps are understood for the restricted Euclidean distances, are as fixed there.

Let U⊆RnU\subseteq\mathbb{R}^{n} be open and convex, let f:U→Rf:U\to\mathbb{R} be convex on UU with subdifferential ∂Uf\partial_{U}f, let y0∈Uy_{0}\in U, and let r,M∈Rr,M\in\mathbb{R} with 0<r0<r and 0≤M0\le M be such that Bˉ(y0,2r)⊆U\bar{B}(y_{0},2r)\subseteq U and

∣f(z)−f(w)∣≤M ∥z−w∥for all z,w∈Bˉ(y0,2r).|f(z)-f(w)|\le M\,\lVert z-w\rVert\qquad\text{for all }z,w\in\bar{B}(y_{0},2r).

Let

S={ (z,q)  :  z∈Bˉ(y0,r)  and  q∈∂Uf(z) },S=\bigl\{\,(z,q)\;:\;z\in\bar{B}(y_{0},r)\ \text{ and }\ q\in\partial_{U}f(z)\,\bigr\},

and for x∈Rnx\in\mathbb{R}^{n} let A(x)={ f(z)+q⋅(x−z)  :  (z,q)∈S }⊆RA(x)=\{\,f(z)+q\cdot(x-z)\;:\;(z,q)\in S\,\}\subseteq\mathbb{R}. Then the following hold.

1. (The extension is well defined) The set SS is nonempty, and for every x∈Rnx\in\mathbb{R}^{n} the set A(x)A(x) is nonempty and bounded above. We may therefore define F:Rn→RF:\mathbb{R}^{n}\to\mathbb{R} by letting F(x)F(x) be the least upper bound of A(x)A(x).

2. (Convexity) FF is convex on Rn\mathbb{R}^{n}.

3. (Agreement on the ball) F(x)=f(x)F(x)=f(x) for every x∈Bˉ(y0,r)x\in\bar{B}(y_{0},r).

4. (Lipschitz bound) FF is Lipschitz with constant MM on Rn\mathbb{R}^{n}; that is, ∣F(x)−F(x′)∣≤M∥x−x′∥|F(x)-F(x')|\le M\lVert x-x'\rVert for all x,x′∈Rnx,x'\in\mathbb{R}^{n}.

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