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Chebyshev stage perturbations have an explicit quadratic bound

Statement

Let d≥1, let A be a self-adjoint endomorphism of Rd with its Euclidean inner product and ⟨Av,v⟩≤0. For first-order RKC assume the integer stage count s≥1 and hρ≤2s2; for linear second-order RKC assume s≥2 and hρ≤2(s2−1)/3, where ρ=max⁡i(−λi(A)). In either branch, let X=I+hA/s2 or X=I+w1hA, respectively.

Write Yj for the generic recurrence stage; in the second-order branch this is the Vj of Second-order linear stabilized Runge–Kutta–Chebyshev step. Model the error injected into each recurrence update by vectors δ1,…,δs: Y^0=y,Y^1=XY^0+δ1,Y^j=2XY^j−1−Y^j−2+δj(2≤j≤s). If ∥δj∥2≤ε for every j, then the final stage error relative to the unperturbed recurrence is at most ∥Y^s−Ys∥2≤s(s+1)2 ε. For linear second-order RKC the returned state multiplies this bound by bs≤1. An additional output perturbation of norm εout adds at most εout. This is a bound for the stated additive-error model; it does not assert a floating-point error size.

Facts & Assumptions

Given: The RKC recurrences, the stage-error vectors and their uniform bound, a self-adjoint nonpositive A, and the corresponding spectral interval condition.

[F1]

First-order stabilized explicit Runge–Kutta–Chebyshev step: in the linear case the stage recurrence is Y0=y, Y1=(I+hA/s2)Y0, and Yj=2(I+hA/s2)Yj−1−Yj−2.

[F2]

Second-order linear stabilized Runge–Kutta–Chebyshev step: the linear RKC2 recurrence uses X=I+w1hA and returns asy+bsVs.

[F3]

Chebyshev polynomials of the first and second kinds by their three-term recurrences: U0=1, U1=2X, and Uk=2XUk−1−Uk−2.

[F4]

Chebyshev bounds and endpoint derivatives used by stabilized Runge–Kutta methods: ∣Uk(x)∣≤k+1 for x∈[−1,1].

[F5]

Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: a self-adjoint endomorphism on finite-dimensional real inner-product space has an orthonormal eigenbasis.

[F6]

Polynomial evaluation at an endomorphism: p(T)=∑kakTk and Orthonormal families, complete orthonormal systems and Hilbert bases: a polynomial acts on an eigenvector by its scalar evaluation at the eigenvalue; orthonormal coordinates give the squared norm as the sum of squared coordinates.

Proof

1.1givenF1F2algebra

Put ej=Y^j−Yj, where Yj is the unperturbed recurrence with the same A,h,y. The first two updates give e0=0 and e1=δ1, and subtraction of the recurrences gives ej=2Xej−1−ej−2+δj for j≥2.

1.2givenF4F5F6algebra

By [F5], choose an orthonormal eigenbasis of A. Under either spectral interval premise, the eigenvalues of X lie in [−1,1]. Thus [F4] and [F6] imply ∥Uk(X)v∥2≤(k+1)∥v∥2 for every vector v: expand in that eigenbasis and bound each squared coordinate.

2.1F3F6step 1.1inductionalgebra

Define Uk(X) by polynomial evaluation [F6]. Induction using [F3] and step 1.1 gives es=∑j=1sUs−j(X)δj. For s=1 this is U0(X)δ1=δ1. At a successor stage, the coefficient of the most recent old perturbation is 2X=U1(X); for every earlier perturbation, multiplying by 2X and subtracting its prior coefficient uses 2XUk(X)−Uk−1(X)=Uk+1(X). The new perturbation has coefficient U0(X)=I. This proves the formula by induction.

3.1givenF7step 2.1step 1.2algebra∎

Apply the triangle inequality [F7] to step 2.1 and then step 1.2: ∥es∥2≤∑j=1s(s−j+1)∥δj∥2≤ε∑k=0s−1(k+1)=s(s+1)2ε. For linear RKC2 the returned perturbation is bses; since 0<bs<1, its norm is at most the same bound. Adding a separate output perturbation uses the triangle inequality once more.

Remarks

The bound grows quadratically with the number of stages. It applies to self-adjoint operators whose scaled eigenvalues stay in the certified interval. It does not control nonnormal transient growth or replace a separate bound on each arithmetic or matrix-action perturbation.

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Sources