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Collocation tableau from distinct quadrature nodes
Definition
Let , let be distinct collocation nodes, and let be their cardinal polynomials from Cardinal-polynomial interpolation. Define The matrix and vector are the collocation tableau coefficients. For an IVP with on the open time-state domain of First-order systems, initial value problems, and solutions on intervals, fix and a step . A stage vector is admissible only when for every . On this admissible set write All displayed integrals use the oriented Riemann convention of The integral with oriented limits: and . In particular when , and the integral in vanishes at . The collocation stages and endpoint are the implicit Runge–Kutta equations of Implicit Runge–Kutta stage equations and update with , nodes , and weights : The associated collocation polynomial on normalized time is Thus , , and whenever is an admissible solution. This definition alone asserts no branch existence or uniqueness.
For interpolation estimates define the Lebesgue constant of this fixed node set by For a real matrix , write . These constants depend on the nodes and on ; no uniform-in- bound is part of the definition.
Depends on
- First-order systems, initial value problems, and solutions on intervals
- Implicit Runge–Kutta stage equations and update
- The integral with oriented limits: $\int_a^a f := 0$ and $\int_b^a f := -\int_a^b f$
- Cardinal-polynomial interpolation
- Cardinal interpolation functions are polynomials of the stated degrees
Used by
- Finite explicit-left-Euler spectral deferred-correction sweep Algorithm
- An explicit SDC sweep diverges on a stiff scalar mode Example
- Two-node Gauss collocation gives the exact endpoint for a nonstiff cubic IVP Example
- A Gaussian collocation defect has a high-order weighted endpoint bound Lemma
- Collocation polynomial derivatives scale with the step size Lemma
- Collocation quadrature data are well defined Lemma
- Fixed points of the SDC sweep are collocation stages Lemma
- One-node Gauss data are midpoint data Lemma
- SDC iteration matrix for the scalar linear test equation Lemma
- One-node Gauss collocation is implicit midpoint of order two Proposition
- A stage residual tolerance preserves collocation order Theorem
- Contraction and residual error bound for the SDC sweep Theorem
- Each SDC sweep raises the order up to the collocation order Theorem
- M-node collocation has local defect order M Theorem
- M-node Gauss collocation has local defect order 2M Theorem
- Small-step collocation maps are Lipschitz stable Theorem
Dependency tree · two levels
26 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, Syvert P. Nørsett, and Gerhard Wanner, Solving Ordinary Differential Equations I, 2nd ed., Chapter II §II.7 (standard reference, not scraped)
- Mathew F. Causley and David C. Seal, On the convergence of spectral deferred correction methods, Communications in Applied Mathematics and Computational Science 14 (2019) (standard reference, not scraped)