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A contractive implicit Runge-Kutta stage branch has a nonsingular residual Jacobian
Statement
Use the implicit Runge–Kutta residual from Implicit Runge–Kutta stage equations and update, where is the input state in that definition. Let , with , be a closed product ball in the admissible stage set. Suppose is on a convex open neighborhood of , and constants satisfy Assume and . Then Small-step implicit Runge–Kutta stages have a unique branch and controlled residual error gives a unique stage root in , and is invertible at every point of . This lemma supplies nonsingularity for implicit differentiation.
Facts & Assumptions
Given: The finite-dimensional stage space, implicit Runge–Kutta residual, selected invariant ball, map , derivative bound on the ball, and .
The stage residual is , where is the input state; a selected stage root satisfies (Implicit Runge–Kutta stage equations and update).
On a closed product ball of radius centered at , bounds and with and give a unique root (Small-step implicit Runge–Kutta stages have a unique branch and controlled residual error).
A vector-valued function continuous on , differentiable on , and with Euclidean derivative norm at most has endpoint difference at most (The mean value inequality: if is continuous and differentiable on with , then ). The chain rule differentiates a map along a segment (The chain rule for total derivatives: ).
For a linear map on a finite-dimensional vector space, (Rank-nullity: ).
Proof
Let . Its convexity puts in for . For each block , the curve is continuous on and differentiable inside because is on an open neighborhood. Its derivative obeys Apply [F3] to each block and take the maximum to obtain . The fixed-point map satisfies , so it maps into itself. Thus [F2] supplies the unique stage root in .
Differentiating [F1] with respect to gives . If , the block row-sum norm and the derivative bound imply Since , this forces ; thus the square finite-dimensional map has trivial kernel.
By [F4], a linear map on the stage space has rank equal to its dimension when its kernel has dimension zero. Therefore is onto as well as one-to-one, hence invertible. This holds at every point of the prescribed branch ball.
Depends on
- Implicit Runge–Kutta stage equations and update
- Small-step implicit Runge–Kutta stages have a unique branch and controlled residual error
- The mean value inequality: if $f : [a,b] \to \mathbb{R}^m$ is continuous and differentiable on $(a,b)$ with $\lVert f'\rVert_2 \le M$, then $\lVert f(b)-f(a)\rVert_2 \le M(b-a)$
- The chain rule for total derivatives: $D(g\circ f)(a)=Dg(f(a))\circ Df(a)$
- Rank-nullity: $\dim_F V=\operatorname{nullity}T+\operatorname{rank}T$
Used by
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Sources
- Ernst Hairer and Gerhard Wanner, Introduction à l’Analyse Numérique, Chapter III, §III.3 (standard reference, not scraped)
- John C. Butcher, Implicit Runge-Kutta Methods, tutorial, §§1–2 (standard reference, not scraped)