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PropositionStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)
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Gradient hessian and divergence connection formulas

Statement

For Levi–Civita, gradf=(df) and the Hessian defined by Hessf(X,Y)=g(Xgradf,Y) is a smooth covariant two-tensor satisfying Hessf(X,Y)=X(Yf)(XY)f,(Hessf)ij=ijfΓkijkf. Moreover the previously defined Riemannian divergence satisfies divgX=tr(vvX)=iXi+ΓiikXk.

Facts & Assumptions

Given: A smooth Riemannian metric, smooth f and vector field X.

[F1]

Gradient is (df), characterized by g(gradf,Y)=Yf (Riemannian gradient).

[F2]

Musical maps commute with Levi–Civita and its dual derivative (Levi civita connection commutes with musical isomorphisms).

[F3]

The Levi–Civita symbols have the metric derivative formula (Christoffel formula for the levi civita connection).

[F4]

Riemannian divergence is (detG)1/2i((detG)1/2Xi) (Coordinate formula for riemannian divergence).

[F5]

The connection is function-linear in its direction and satisfies the section Leibniz rule (Connection laws in directional form).

Proof

1.1

By [F1] and [F2], g(Xgradf,Y)=(Xdf)(Y)=X(df(Y))df(XY)=X(Yf)(XY)f. The original metric expression is function-linear in both X and Y by direction-linearity and fibrewise metric linearity, so it is a smooth two-tensor. Substituting coordinate fields gives the displayed Hessian coefficients.

F1F2F5
1.2

For a fixed field X, direction-linearity makes vvX a smooth fibre endomorphism. In a coordinate basis, iX=(iXj+ΓjikXk)j, so its trace is iXi+ΓiikXk. Trace is basis independent: for square matrices A,B, the finite sums give tr(AB)=ijAijBji=tr(BA), hence tr(S1AS)=trA.

F5given
1.3

Contract the Christoffel formula to obtain Γiik=12gikgi: its first and third derivative terms cancel by exchanging i, and using symmetry of G1. For an invertible differentiable matrix G, write each differentiated column as a linear combination of the original columns, with coefficient matrix G1kG. Multilinearity of determinant shows that replacing one column contributes only its own diagonal coefficient, since every other replacement repeats another column. Consequently kdetG=(detG)tr(G1kG), and the scalar square-root derivative gives klogdetG=Γiik.

F3
2.1

Expanding [F4] with the scalar product rule now gives precisely the trace in step 1.2. Thus the trace agrees with the existing divergence, including nonorientable manifolds; no normal coordinates are used. Constant f has zero Hessian, and X=0 has zero divergence. In dimension zero all sums are empty; in dimension one the same scalar formulas hold. Positive definiteness keeps the determinant positive; smooth boundary derivatives obey the same formulas.

F4step 1.1step 1.2step 1.3

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