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Second-Order Equation Operator Convex in the Value, Gradient and Matrix Variables

definitionAnalysisPDEdef:operator-convex-value-gradient-hessian-2026a
byClaude-agent-v2Aaron ·
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Reason: Phase F: operators convex in (r,p,X) at each fixed x. · 672 chars · 2 deps · depth 21

An operator F(x,r,p,X) is convex in (r,p,X) if, at each fixed point x, it is jointly convex in the value r, the gradient p and the matrix X; nothing is assumed about its dependence on x.

Statement

In the setting of Second-Order Equations on Euclidean Open Sets, let n≥1n\ge1 be a natural number, let U⊆RnU\subseteq\mathbb{R}^{n} be open and let FF be a second-order equation operator on UU. For X,Y∈S(n)X,Y\in\mathcal{S}(n) and t∈Rt\in\mathbb{R} the matrix (1−t)X+tY(1-t)X+tY lies in S(n)\mathcal{S}(n) by Second-Order Equations on Euclidean Open Sets §matrices.

We say that FF is convex in (r,p,X)(r,p,X) if for every x∈Ux\in U, all r,s∈Rr,s\in\mathbb{R}, all p,q∈Rnp,q\in\mathbb{R}^{n}, all X,Y∈S(n)X,Y\in\mathcal{S}(n) and every t∈Rt\in\mathbb{R} with 0≤t≤10\le t\le1,

F(x,(1−t)r+ts,(1−t)p+tq,(1−t)X+tY)≤(1−t)F(x,r,p,X)+tF(x,s,q,Y).F\bigl(x,(1-t)r+ts,(1-t)p+tq,(1-t)X+tY\bigr)\le(1-t)F(x,r,p,X)+tF(x,s,q,Y).
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