Alphabeta Math
TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-02
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A total derivative computes every directional derivative, and its matrix is the Jacobian

Statement

If f:U→Rn is totally differentiable at a, then Dvf(a) exists for every v∈Rm and equals Df(a)v. In particular ∂jf(a)=Df(a)ej, and the matrix of Df(a) is Jf(a).

Facts & Assumptions

Given: A total derivative Df(a) and a direction v∈Rm.

[L1]

In the total-derivative definition, the normalized remainder tends to zero as h tends to zero (The total (Fréchet) derivative Df(a) as the linear first-order approximation with o(∥h∥2) remainder).

[L2]

The directional derivative is the derivative of t↦f(a+tv) at zero, and partial derivatives use standard-basis directions (Directional derivatives and partial derivatives of a map U⊆Rm→Rn).

Proof

technique · direct
1.1

For t≠0, write f(a+tv)−f(a)=tDf(a)v+r(tv), where ∥r(tv)∥2/∥tv∥2→0 by [L1].

L1L2
2.1

Dividing by t gives (f(a+tv)−f(a))/t=Df(a)v+r(tv)/t, and ∥r(tv)/t∥2=∥v∥2 ∥r(tv)∥2/∥tv∥2→0; hence [L2] yields Dvf(a)=Df(a)v.

step 1.1L2algebra
3.1

Taking v=ej identifies the jth column of the matrix of Df(a) with the vector of jth partial derivatives, which is precisely the Jacobian.

step 1.1step 2.1∎

Depends on

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Dependency tree · two levels

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