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Invertible linear substitutions preserve Schwartz space

Statement

Let A be an invertible real n×n matrix and put (A∗f)(y):=f(Ay). If f∈S(Rn) then A∗f∈S(Rn); more precisely, for every pair of multi-indices α,β there are a constant Cαβ and a finite set of Schwartz seminorms of f with pαβ(A∗f)≤Cαβmax⁡∣δ∣≤∣α∣, ∣γ∣≤∣β∣pδγ(f). Hence f↦f∘A is a continuous linear endomorphism of S(Rn) with continuous inverse f↦f∘A−1. No choice principle is used.

Facts & Assumptions

Given: An invertible real n×n matrix A, a function f∈S(Rn) as in Schwartz space and its seminorms, and the multi-index derivative notation of Ck maps and multi-index derivative notation in Euclidean space.

[F1]

S(Rn)⊆C∞(Rn;C), and pαβ(g)=sup⁡x∈Rn∣xα∂βg(x)∣ for g∈S(Rn) (Schwartz space and its seminorms); a map is Ck when all iterated coordinate derivatives of order at most k exist and are continuous, C∞ meaning Ck for every k (Ck maps and multi-index derivative notation in Euclidean space).

[F2]

Totally differentiable maps and their total derivative are as in The total (Fréchet) derivative Df(a) as the linear first-order approximation with o(∥h∥2) remainder, and D(g∘h)(a)=Dg(h(a))∘Dh(a) (The chain rule for total derivatives: D(g∘f)(a)=Dg(f(a))∘Df(a)); if all partial derivatives of a map exist on a neighbourhood of a point and are continuous there, the map is totally differentiable at that point with derivative the Jacobian matrix (If all partial derivatives exist on a neighbourhood and are continuous at a point, then the map is totally differentiable there with Jacobian derivative, The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case).

[F3]

Finite sums and scalar multiples, and composites, of Ck Euclidean maps are Ck (Ck Euclidean maps are closed under componentwise algebra and composition).

[F4]

Every linear map L:Rm→Rn has a unique matrix with (Lh)i=∑j<maijhj and satisfies ∥Lh∥2≤K∥h∥2 for some K≥0 (Every Euclidean linear map has a unique matrix and satisfies ∥Lh∥2≤K∥h∥2 for some K≥0); A is invertible, so h↦A−1h is defined (A finite square real matrix is invertible if and only if its determinant is nonzero), and matrix-vector multiplication is the matrix product of Rectangular matrix multiplication and the identity matrix In, including zero-sized shapes.

[F5]

The Schwartz topology has as a base of neighbourhoods of a point the finite intersections of conditions pαβ(g−g0)<ε (Schwartz topology and convergence).

[F6]

For a natural number N and reals u0,…,um−1, the expansion (u0+⋯+um−1)N=∑∣δ∣=N(Nδ)∏iuiδi holds (The multinomial coefficient equals n!/∏i<mki!, and (x0+⋯+xm−1)n=∑ι ⁣(nk)∏i<mxiki in R).

[F7]

For a Ck real scalar field, k≥2, every ordered derivative of order k is unchanged by permuting the coordinate differentiations (Continuous mixed partials of order k are invariant under permutations). Applying this to the real and imaginary parts gives the same assertion for smooth complex functions, so ∂j∂γf=∂γ+ejf.

Proof

technique · direct
1.1F1F2F3F4given

The map y↦Ay is C∞: each component ∑jAijyj is a finite sum of scalar multiples of the coordinate functions, whose iterated coordinate derivatives are constant, hence continuous, so each component is C∞ as a composite of the C∞ identity with itself and finite sums of such [F1, F3]. It is totally differentiable at every y with D(A⋅)(y)=A, because A(y+h)=Ay+Ah has remainder identically zero in the defining limit [F2]. Consequently f∘A∈C∞ [F1, F3], and for every C1 map u on Rn and every j, the chain rule gives ∂j(u∘A)(y)=(Du(Ay)∘A)ej, and the matrix of Du(Ay) is the Jacobian (∂iu(Ay)) because the partial derivatives of u are continuous [F2, F4], so ∂j(u∘A)(y)=∑iAij(∂iu)(Ay).

2.1step 1.1F1F7algebra

Iterating the coordinate chain rule of step 1.1 along the canonical differentiation word for β gives a finite sum of derivatives of f of order ∣β∣, evaluated at Ay, with constant coefficients depending only on A. By [F7] those derivatives can be grouped by their multi-indices, giving ∂β(f∘A)(y)=∑∣γ∣=∣β∣cβγ(∂γf)(Ay). The case β=0 has its single coefficient equal to one.

3.1step 2.1F1F4F6algebra

Choose K≥1 with ∥A−1x∥2≤K∥x∥2 [F4], and put N=∣α∣. For x=Ay one has ∣yα∣≤KN(1+∑i∣xi∣)N. By [F6] this last power is ∑∣δ∣≤NbNδ∣xδ∣, where bNδ:=N!/((N−∣δ∣)! δ!) is the multinomial coefficient with exponent tuple (N−∣δ∣,δ1,…,δn). Combining this expansion with step 2.1 and taking the supremum over y gives pαβ(A∗f)≤KN∑∣δ∣≤NbNδ∑∣γ∣=∣β∣∣cβγ∣pδγ(f), hence the asserted finite-maximum bound with Cαβ:=KN∑∣δ∣≤NbNδ∑∣γ∣=∣β∣∣cβγ∣.

4.1step 3.1F4F5algebra∎

Every seminorm pαβ(A∗f) is finite by step 3.1, so A∗f∈S(Rn); and the same step with A replaced by A−1 shows (A−1)∗f∈S(Rn), while (A−1)∗(A∗f)=f=(A∗)(A−1)∗f. For continuity, fix a basic neighbourhood pαrβr(g)<εr (r≤m) of 0 in the Schwartz topology [F5]; by step 3.1 the preimage under f↦A∗f contains the neighbourhood of 0 cut out by the finitely many conditions pδγ(f)<εr/max⁡(Cαrβr,1) over the (∣δ∣≤∣αr∣,∣γ∣≤∣βr∣) appearing in the r-th estimate, so the map is continuous at 0 and, being linear, everywhere. The same argument applies to f↦f∘A−1. No choice is used: all sums, constants and maxima above range over finite index sets determined by α,β and the fixed matrix A.

The theorem Basic operations are continuous on Schwartz space includes reflection, the special case A=−I, but does not assert continuity for arbitrary invertible linear substitutions. The argument above establishes the general case directly, without using that theorem as a supplier.

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