Alphabeta Math
TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-02
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On a convex open set, a uniform bound ∥Df(z)v∥2≤M∥v∥2 implies ∥f(y)−f(x)∥2≤M∥y−x∥2

Statement

Let U⊆Rm be convex and open, and let f:U→Rn be totally differentiable at every point. If M≥0 satisfies ∥Df(z)v∥2≤M∥v∥2 for every z∈U and v∈Rm, then

∥f(y)−f(x)∥2≤M∥y−x∥2(x,y∈U).

Facts & Assumptions

Given: The stated convex open domain, total differentiability, and uniform derivative bound.

[L1]

A convex subset contains every line segment between two of its points (A convex subset of Rm contains every line segment between two of its points).

[L2]

The chain rule for total derivatives is D(g∘f)(a)=Dg(f(a))∘Df(a) (The chain rule for total derivatives: D(g∘f)(a)=Dg(f(a))∘Df(a)).

[L3]

The vector mean-value inequality gives ∥f(b)−f(a)∥2≤M(b−a) when the derivative norm is bounded by M (The mean value inequality: if f:[a,b]→Rm is continuous and differentiable on (a,b) with ∥f′∥2≤M, then ∥f(b)−f(a)∥2≤M(b−a)).

[L4]

Total differentiability implies continuity at the point of total differentiability (Total differentiability gives a local O(∥h∥2) increment bound and therefore continuity).

Proof

technique · direct
1.1

If x=y the conclusion is immediate. Otherwise put γ(t)=x+t(y−x) for 0≤t≤1; [L1] keeps γ([0,1]) in U.

L1L2L3
2.1

The chain rule gives (f∘γ)′(t)=Df(γ(t))(y−x) for 0<t<1, whose norm is at most M∥y−x∥2 by hypothesis.

step 1.1L2algebra
3.1

By [L4] the curve f∘γ is continuous at the endpoints, so [L3] applied on [0,1] yields ∥f(y)−f(x)∥2≤M∥y−x∥2.

step 1.1step 2.1L3L4∎

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