Alphabeta Math
CorollaryStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-08-24
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  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

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For one regular constraint, the objective gradient is a scalar multiple of the constraint gradient

Statement

Let f,G:U⊆Rm→R be C1, and suppose a is a local maximum or minimum of f subject to G(x)=c. If ∇G(a)≠0, then there is a unique scalar λ such that ∇f(a)=λ∇G(a).

Facts & Assumptions

Given: The functions, constrained local extremum, and nonzero constraint gradient.

[L1]

For a scalar function, the Jacobian is the row ∇G(a)T (The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case), so DG(a):Rm→R is surjective exactly when ∇G(a)≠0.

[L2]

At a constrained local extremum with DG(a) surjective, there is a unique λ∈R such that ∇f(a)=DG(a)Tλ (Lagrange multipliers for a regular vector-valued level-set constraint).

Proof

technique · direct
1.1givenL1

By [L1], the nonzero-gradient hypothesis makes DG(a) surjective.

2.1step 1.1L2algebra

Apply [L2]. Since the transpose of the one-row matrix DG(a) sends λ to λ∇G(a), its conclusion is the displayed equation.

3.1step 2.1∎

Uniqueness is part of [L2] and also follows directly from ∇G(a)≠0.

Depends on

Used by

Dependency tree · two levels

12 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources