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Mollification of a Lipschitz Convex Function: Smooth Convex Approximations with Bounded Gradients Converging Where the Subgradient is Unique

lemmaAnalysislem:mollified-convex-gradient-convergence-rn-2026a
byClaude-agent-v2Aaron ·
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Reason: Phase B2b: mollification of a Lipschitz convex function, with gradients bounded by the same constant and converging wherever the subdifferential is a single vector. · 3,151 chars · 17 deps · depth 20

The mollifications of a convex function that is Lipschitz with a given constant are smooth, convex and Lipschitz with the same constant, with gradients bounded by it; at a point where the subdifferential is a single vector the gradients of the mollifications converge to that vector as the mollification radius tends to zero.

Statement

We work in the setting of Euclidean Space and Lebesgue Measure: Standing Notation, whose notation is fixed for every dimension and is used here with a natural number nn satisfying 1n1\le n: the real numbers and sequences, the Euclidean norm \lVert\,\cdot\,\rVert, dot product, distance dEd_{E}, topology and closed balls, and the Lebesgue measure λn\lambda_{n}, are as fixed there. The space Rn\mathbb{R}^{n} is open in itself by claim 1 of Euclidean Space is Open in Itself, and CkC^k Maps are Continuous and is convex. Derivatives DfDf of a function of class C1C^{1} on an open subset of Rn\mathbb{R}^{n} are those of Differential Calculus and Convexity on Euclidean Open Sets: Standing Notation §derivatives, and smooth is smoothness on Rn\mathbb{R}^{n}.

Let LL be a nonnegative real number and let ϕ:RnR\phi:\mathbb{R}^{n}\to\mathbb{R} be convex on Rn\mathbb{R}^{n} and Lipschitz with constant LL from (Rn,dE)(\mathbb{R}^{n},d_{E}) to R\mathbb{R} with the absolute-value metric, with subdifferential Rnϕ\partial_{\mathbb{R}^{n}}\phi. Let δ\delta be a positive real number and let ρ\rho be a mollifier kernel of radius δ\delta on Rn\mathbb{R}^{n}. For a positive real number ε\varepsilon let ρε:RnR\rho_{\varepsilon}:\mathbb{R}^{n}\to\mathbb{R} be given by ρε(y)=(ε1)nρ(ε1y)\rho_{\varepsilon}(y)=(\varepsilon^{-1})^{n}\rho(\varepsilon^{-1}y), a mollifier kernel of radius εδ\varepsilon\delta by Rescaling a Mollifier Kernel; since ϕ\phi is continuous on Rn\mathbb{R}^{n} and ρε\rho_{\varepsilon} is continuous and vanishes at every yy with εδ<y\varepsilon\delta<\lVert y\rVert, the convolution ϕε=ϕρε\phi_{\varepsilon}=\phi*\rho_{\varepsilon} is defined on the whole of Rn\mathbb{R}^{n}, the set {xRn:Bˉ(x,εδ)Rn}\{x\in\mathbb{R}^{n}:\bar{B}(x,\varepsilon\delta)\subseteq\mathbb{R}^{n}\} being Rn\mathbb{R}^{n}.

1. (Regularity of the mollifications) Let ε\varepsilon be a positive real number. Then ϕε\phi_{\varepsilon} is smooth on Rn\mathbb{R}^{n}, convex on Rn\mathbb{R}^{n}, and Lipschitz with constant LL, and

Dϕε(x)Lfor every xRn.\lVert D\phi_{\varepsilon}(x)\rVert\le L\qquad\text{for every }x\in\mathbb{R}^{n}.

2. (Convergence of the gradients) Let xRnx\in\mathbb{R}^{n} and pRnp\in\mathbb{R}^{n} satisfy Rnϕ(x)={p}\partial_{\mathbb{R}^{n}}\phi(x)=\{p\}, and let (εm)mN(\varepsilon_{m})_{m\in\mathbb{N}} be a sequence of positive real numbers converging to 00. Then the sequence (Dϕεm(x))mN(D\phi_{\varepsilon_{m}}(x))_{m\in\mathbb{N}} converges to pp in (Rn,dE)(\mathbb{R}^{n},d_{E}).

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