Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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.

Neutral derivative feedback amplifies outside the stable branch

Example

For any a∈R, let q(s)=1+(a−1)(s+τ)/τ and ϕ(s)=(s+τ)+(a−1)(s+τ)2/(2τ) on [−τ,0]. Consider y′(t)−ay′(t−τ)=0,y∣[−τ,0]=ϕ. This is the scalar neutral problem of Scalar neutral delay equations and the derivative-segment shift. Its history is compatible at zero. On each later delay segment its derivative is multiplied by a: y′(kτ+s)=ak+1q(s−τ),0≤s≤τ. Thus for ∣a∣>1, derivative norms grow geometrically from segment to segment. At the boundary a=1, the finite-horizon shift inverse exists but its norm grows with the number K of segments. The uniform resolvent bound in The neutral derivative shift has a bounded Neumann inverse when ∣a∣<1 is therefore restricted to ∣a∣<1.

Facts & Assumptions

Given: A positive delay τ, the compatible history above, real a, and a finite horizon T=Kτ with integer K≥1.

[F1]

The scalar neutral equation is y′(t)−ay′(t−τ)=f(t,y(t),y(t−τ)), and compatibility is ϕ′(0)=aϕ′(−τ)+f(0,ϕ(0),ϕ(−τ)) (Scalar neutral delay equations and the derivative-segment shift).

[F2]

Compatible C1 history and continuous state-Lipschitz f give a unique solution by method of steps (Method of steps gives a unique compatible neutral-delay solution).

[F3]

When ∣a∣<1, the segment shift inverse has norm at most (1−∣a∣)−1 (The neutral derivative shift has a bounded Neumann inverse when ∣a∣<1).

Proof

1.1F1F2givenalgebra

Differentiating the displayed polynomial gives ϕ′=q, q(−τ)=1, and q(0)=a. With f=0, the compatibility equation [F1] reads a=a⋅1+0, so it holds. The zero right-hand side is continuous and Lipschitz in the two state variables. Therefore [F2] supplies the unique solution on every finite horizon Kτ.

2.1F1step 1.1inductionalgebra

On the first segment, the equation gives y′(s)=aϕ′(s−τ)=aq(s−τ). If on segment k−1 the derivative is akq(s−τ), then on segment k the same equation gives y′(kτ+s)=ay′((k−1)τ+s)=ak+1q(s−τ). Compatibility makes the left and right endpoint values agree at every breakpoint, so induction proves the formula for 0≤k<K. Since q(−τ)=1, each segment derivative has supremum at least ∣a∣k+1; for ∣a∣>1 this is geometric amplification.

3.1F1F3givenalgebra∎

For a=1, on the K-segment space from [F1], let rk(s)=1 and uk(s)=k+1 for 0≤k<K. The defined shift has (SKu)0=0 and (SKu)k=uk−1 for k≥1, so (I−SK)u=r. Also SKK=0, hence (I−SK)−1=I+SK+⋯+SKK−1 by multiplying the finite sum. Since ∥r∥XK=1 and ∥u∥XK=K, the inverse norm is at least K. Thus no horizon-independent bound follows at a=1, consistent with the strict hypothesis ∣a∣<1 in [F3].

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

14 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