How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- 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.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Implicit differentiation and adjoint formulas under a nonsingular residual Jacobian
Statement
Let be , where and are open. Suppose and the state Jacobian is invertible. The Euclidean implicit function theorem gives a local solution branch , and For a scalar objective , its derivative along that branch is Equivalently, if solves , this derivative is . These formulas concern the exact local solution branch; differentiating a stopped iteration is a separate finite-map derivative.
Facts & Assumptions
Given: The residual equation, a residual, a square nonsingular state Jacobian, and a scalar objective.
The Euclidean implicit function theorem with derivative formula: when both block dimensions are positive, a Euclidean residual with zero value and invertible state-block derivative has a unique local solution branch and .
The chain rule for total derivatives: : for maps , .
Proof
If , the state space is a singleton, its unique linear map is its own two-sided inverse, and the sole branch has zero derivative, as does the displayed formula. If and , the parameter space is a singleton. Write . Differentiability gives ; since , this has no other zero for sufficiently small nonzero . Thus the singleton branch is locally unique and has zero derivative, again the displayed formula. If , apply [F1] to obtain the local branch and .
The map is . By [F2], its derivative at is . Substituting step 1.1 yields .
Since is invertible, is invertible. The adjoint equation has the unique solution satisfying , by transposing . Therefore , giving the stated adjoint formula.
Depends on
Used by
- Finite-horizon shooting and Runge–Kutta collocation transcription Algorithm
- An implicit linear solve and its adjoint gradient Example
- Certified inverse defects and primal residuals bound linear-solve Jacobian and pullback errors Lemma
- An implicit Runge-Kutta stage has the stated parameter sensitivity on its selected branch Theorem
Dependency tree · two levels
10 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
- J. Lebl, Basic Analysis II, Theorem 8.5.6 and proof, §8.5.1 (standard reference, not scraped)
- J. Lebl, Basic Analysis I, Theorem 8.3.7 and proof, §8.3.1 (standard reference, not scraped)