TheoremBase

Second-Order Test Data at a Global Quadratic Maximum of a Semiconvex Function

For a semiconvex function on RN\mathbb{R}^N whose difference with a quadratic form attains a maximum at the origin, produces points of twice differentiability approaching the origin whose gradients tend to zero and whose Hessians converge to a symmetric matrix XX with −λI⪯X⪯B-\lambda I \preceq X \preceq B, and shows that XX is admissible test data from above at the origin.

Statement

Throughout we work in the setting of Differential Calculus and Convexity on Euclidean Open Sets: Standing Notation, whose notation, including that of Euclidean Space and Real Matrices: Standing Notation on which it rests, is in force in the dimension NN, a natural number with 1≤N1\le N. In particular −aIN-aI_{N} denotes (−a)IN(-a)I_{N}, an element of S(N)\mathcal{S}(N) for every real aa, as fixed in Euclidean Space and Real Matrices: Standing Notation (iii), and convergence of a sequence in RN\mathbb{R}^{N} or in S(N)\mathcal{S}(N) refers to dEd_{E} or to dS(N)d_{\mathcal{S}(N)} respectively, as fixed in Euclidean Space and Real Matrices: Standing Notation (v). We abbreviate ∥z∥2=∥z∥⋅∥z∥\lVert z\rVert^{2}=\lVert z\rVert\cdot\lVert z\rVert, and s2\tfrac{s}{2} denotes the product of s∈Rs\in\mathbb{R} with the multiplicative inverse of 2=1+12=1+1, which exists by claim 8 of Elementary Order Arithmetic in an Ordered Field. The set RN\mathbb{R}^{N} is open, directly from Open Subset of a Metric Space, and is convex, directly from that definition. That a quadruple is approximable by test data from above for a function on an open set is as defined there.

Let λ∈R\lambda\in\mathbb{R} satisfy 0≤λ0\le\lambda, let f:RN→Rf:\mathbb{R}^{N}\to\mathbb{R} be semiconvex on RN\mathbb{R}^{N} with constant λ\lambda, and let B∈S(N)B\in\mathcal{S}(N). Assume that

f(ξ)−12 ξ⋅(Bξ) ≤ f(0RN)for every ξ∈RN.f(\xi)-\tfrac{1}{2}\,\xi\cdot(B\xi)\ \le\ f\bigl(0_{\mathbb{R}^{N}}\bigr)\qquad\text{for every }\xi\in\mathbb{R}^{N}.

Then the following hold.

1. (An approximating sequence of points of twice differentiability) There exist X∈S(N)X\in\mathcal{S}(N) and a sequence (xk)k∈N(x_{k})_{k\in\mathbb{N}} in RN\mathbb{R}^{N} such that ff is twice differentiable, with gradient Df(xk)Df(x_{k}) and Hessian D2f(xk)D^{2}f(x_{k}), at xkx_{k} for every k∈Nk\in\mathbb{N}, the sequence (xk)k∈N(x_{k})_{k\in\mathbb{N}} converges to 0RN0_{\mathbb{R}^{N}} in RN\mathbb{R}^{N}, the sequence (Df(xk))k∈N\bigl(Df(x_{k})\bigr)_{k\in\mathbb{N}} converges to 0RN0_{\mathbb{R}^{N}} in RN\mathbb{R}^{N}, the sequence (D2f(xk))k∈N\bigl(D^{2}f(x_{k})\bigr)_{k\in\mathbb{N}} converges to XX in S(N)\mathcal{S}(N), and

−λIN ⪯ X ⪯ B.-\lambda I_{N}\ \preceq\ X\ \preceq\ B .

2. (Test data at the origin) Let X∈S(N)X\in\mathcal{S}(N) and let (xk)k∈N(x_{k})_{k\in\mathbb{N}} be a sequence in RN\mathbb{R}^{N} with the properties listed in claim 1. Then the quadruple

(0RN, f(0RN), 0RN, X)\bigl(0_{\mathbb{R}^{N}},\,f(0_{\mathbb{R}^{N}}),\,0_{\mathbb{R}^{N}},\,X\bigr)

is approximable by test data from above for ff, the domain being RN\mathbb{R}^{N}. In particular there is X∈S(N)X\in\mathcal{S}(N) with −λIN⪯X⪯B-\lambda I_{N}\preceq X\preceq B for which that quadruple is approximable by test data from above for ff.

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