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.
One-step ODE methods, local defect, uniform order, and step stability
Definition
Let , let be open, and consider a first-order IVP and solution in the sense of First-order systems, initial value problems, and solutions on intervals. A one-step method is a family of maps , defined for admitted times , positive step sizes , and states , whose value is the method's approximation at time from the single input state at time . It does not use earlier numerical states.
If is an exact solution on an interval containing , the local defect is
Let be a fixed compact time-state set. The method has uniform local order on along if there are constants and , independent of the admitted starting time and step , such that
whenever the exact solution graph on and the associated method time-state inputs, stages, and outputs under consideration lie in . Here is a positive integer and is the Euclidean norm of The Euclidean inner product on .
The method has one-step Lipschitz stability on with constant if
for every admitted step size , admitted time , and input states with . This is a bound on the change in one step; it is separate from absolute or linear stability regions and does not itself assert a global error estimate.
Depends on
Used by
- Age-cohort quadrature along exact characteristics Algorithm
- First-order stabilized explicit Runge–Kutta–Chebyshev step Algorithm
- Second-order linear stabilized Runge–Kutta–Chebyshev step Algorithm
- Second-order nonlinear stabilized Runge–Kutta–Chebyshev step Algorithm
- The three-stage third-order SSP Runge–Kutta step Algorithm
- Embedded Runge–Kutta pair and difference estimator Definition
- Explicit Runge–Kutta tableaus, stage maps, and failure status Definition
- A breakpoint-crossing RK4 step has a second-order defect Example
- A time kink reduces the explicit midpoint local order Example
- Explicit midpoint loses its uniform second-order defect bound at a time kink Example
- Forward Euler on the scalar linear equation y'=y Example
- Modified Patankar Euler has a uniform first-order defect and one-step bound on positive tubes Lemma
- MPRK22(1) has a uniform second-order defect and one-step bound on positive tubes Lemma
- Orders and scalar stability functions of Euler, midpoint, and classical RK4 Proposition
- SSPRK(3,3) is third order and preserves conditional convex bounds Proposition
- Sufficient explicit Runge–Kutta order conditions through order four Proposition
- Forward Euler has uniform local order one and a one-step Lipschitz bound Theorem
- Fourth-order global error for RK4 on a regularized vortex particle tube Theorem
- Global error from uniform one-step defect and stability Theorem
- Grid-aligned Euler has first-order error for retarded delays Theorem
- Local orders and leading estimator term for the Euler–Heun pair Theorem
- Modified Patankar Euler converges globally with first-order error on a positive tube Theorem
- MPRK22(1) has global second-order error on a compact positive tube Theorem
- The factorized zero-state step is well-defined, nonnegative, conservative, and locally first order Theorem
- Undamped nonlinear RKC2 has fixed-stage order two on an admitted tube Theorem
Dependency tree · two levels
17 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
- Ernst Hairer, course notes, Chapter III: Équations Différentielles Ordinaires (standard reference, not scraped)
- MIT OpenCourseWare, 18.330 Introduction to Numerical Analysis (Spring 2012), Chapter 5 (standard reference, not scraped)
- Tobin A. Driscoll and Richard J. Braun, Fundamentals of Numerical Computation, Julia Edition, Section 6.2 (standard reference, not scraped)