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.
First-order stabilized explicit Runge–Kutta–Chebyshev step
Definition
This algorithm takes an autonomous vector field on an open state domain , an admitted initial state , a step , an integer stage count , and an evaluator that returns either or an evaluation-failure status. In the exact mathematical model the evaluator returns at every ; the failure status records only an unavailable numerical evaluation. Define stage abscissae for . Set . Before each query check that its stage state belongs to ; if it does not, return domain-failure with that stage index. If the evaluator reports failure, return evaluation-failure with that stage index. Query at and, on success, set . For , query at and, on success, set If the final state is outside , return output-domain-failure; otherwise return success with state .
For a nonautonomous field on an open time-state domain , input an admitted pair and an evaluator that returns either or evaluation-failure. In the exact mathematical model it returns at every point of . Use the same state recurrence, querying at the declared stage times: query at before setting ; for , query at before setting Before each query, check that its time-state input lies in ; return domain-failure with that stage index if it does not, or evaluation-failure with that index if the evaluator fails. After the stages, return output-domain-failure if ; otherwise return success with . The scalar linear stability polynomial is established separately.
The stability analysis uses and from Chebyshev polynomials of the first and second kinds by their three-term recurrences; this item defines the executable recurrence and does not itself assert an order or stability theorem.
Depends on
Used by
- A self-adjoint heat spectrum gives a long RKC1 stability step Example
- Equal additive stage perturbations attain quadratic RKC1 amplification Example
- Scalar eigenvalue stability does not control nonnormal matrix growth Example
- Chebyshev stage perturbations have an explicit quadratic bound Lemma
- RKC stability polynomials and certified negative-real intervals Proposition
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
- Ibrahim Almuslimani and Gilles Vilmart, Explicit Stabilized Integrators for Stiff Optimal Control Problems, §2.2.1 (standard reference, not scraped)
- B.P. Sommeijer, L.F. Shampine, and J.G. Verwer, RKC: An explicit solver for parabolic PDEs, §2 (standard reference, not scraped)