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Distributions Test Functions and Differentiation

1 · Prerequisites

2 · Summary

Tests are smooth functions with compact support. Their topology controls every derivative while allowing the support to vary through compact subsets of the domain. We construct that topology from its fixed-support spaces, prove the local finite-order criterion for continuous linear functionals, and use it to define distributions and their derivatives.

Localization is proved before support, point-supported distributions and compact-support extensions. Bounded test sets have one common compact support and uniform derivative bounds; this description makes the weak and strong distribution topologies concrete. Smooth parameter pairing then supports convolution and tensor products. Product-test density is proved before tensor uniqueness, so neither construction presupposes distributional mollification.

Choice assumptions are stated where used. The LF construction, finite-order estimates, support and sheaf arguments, tensor products, and compact-factor convolution identities are choice-free. Countable Choice supplies the Lebesgue-measure interfaces for regular distributions, classical derivative compatibility, parameter integration and mollification. The pointwise-bounded-family theorem uses Dependent Choice; continuous primitive structure uses the Axiom of Choice for functional extension and representation. Closed bounded test sets are proved compact under the stated Countable Choice and Dependent Choice assumptions.

The final results give local mollifier convergence, compactly supported smooth approximation, constants characterized by zero first derivatives, and the precise support condition for convolution associativity. The companion calculations include jump masses, the Newtonian kernel, principal value, concentration, and sharp orders of Dirac derivatives. Sobolev norms and weak-solution estimates remain in the PDE track.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Test function space d of an open set

Definition

Let ΩRn be open, with n1. A complex-valued function is smooth if its real and imaginary parts are Ck for every finite k, in the sense of Ck maps and multi-index derivative notation in Euclidean space. Coordinate derivatives act on these two parts separately. We write α for the multi-index convention there, and 0φ=φ.

The test-function space is D(Ω)=Cc(Ω;C): its members are smooth functions whose support, the closure in Ω of their nonzero set, is a compact subset of Ω. Equivalently, the zero extension to Rn is smooth with compact support contained in Ω: compactness gives a neighborhood of every boundary point disjoint from the support, where that extension vanishes. Functions are actual functions, not equivalence classes.

Pointwise addition and scalar multiplication make this a complex vector space; the support of a sum lies in the union of the two compact supports. The zero function has empty support. If Ω= there is just the empty function, which is the zero vector. The topology is specified in subsequent definitions. Distribution pairings throughout this page are complex bilinear, with no conjugation on test functions. One may restrict all constructions to real scalars.

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Fixed support test function frechet space

Definition

For an integer n1, a compact KΩ, and an open set ΩRn, put DK={φD(Ω):suppφK}, using Test function space d of an open set. For integers m0 set

pm(φ)=maxαmsupxKαφ(x),d(φ,ψ)=m=02m1min(1,pm(φψ)).

The topology is generated by the seminorms pm: neighborhoods at zero contain finite intersections of pm<ε with ε>0. Derivatives are continuous on K, so these suprema are finite; for K= all suprema are defined as zero and DK={0}. For nonempty K, p0 separates functions because all functions vanish off K. These are increasing seminorms and the displayed series converges since its terms are at most 2m1.

The term fixed-support Fréchet space refers to this topology and metric. Their agreement and completeness are established by the registered justifier Fixed support test function spaces are complete , rather than included as unproved consequences of the name. If K has empty interior, every test supported in K is zero: a nonzero continuous value would have a neighborhood of nonzero values inside K.

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Test function topology

Definition

Let ΩRn be open. For the fixed-support spaces of Fixed support test function frechet space, let P be the set of all seminorms q:D(Ω)[0,) such that qDK is continuous for every compact KΩ. A seminorm means q(av)=aq(v) and q(v+w)q(v)+q(w).

The test-function topology is the topology generated by translated finite intersections of sets {φ:q(φ)<ε}, where qP and ε>0. Equivalently it is the finest locally convex topology making every inclusion DKD(Ω) continuous; the universal-property lemma below proves this equivalence and the topology assertions. This is the locally convex inductive-limit or LF topology, not the unrestricted final topology of arbitrary spaces.

For precision, a locally convex topology here is a vector topology generated by a family of seminorms, and boundedness means absorption by every zero-neighborhood: for each such neighborhood U there is t>0 with BtU. Finite intersections may be empty, giving the whole space. When Ω is empty there is only the zero vector and its unique topology. No convergence criterion for sequences is used in the definition; that criterion will be a theorem.

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Distribution

Definition

For open ΩRn, a distribution is a continuous complex-linear map u:D(Ω)C, with the test-function topology of Test function topology. Their vector space is denoted D(Ω). Write u,φ=u(φ); the pairing is linear in both arguments, with no conjugation.

Continuity means continuity for that topology: for each ε>0 the set {u(φ)<ε} contains a zero-neighborhood. By linearity this suffices at every point. Sums and scalar multiples remain continuous and linear, so the indicated collection is a complex vector space. If Ω=, its test space is zero and its distribution space is zero as well. The finite-order estimate and the sequential criterion are established separately; neither substitutes for continuity in this definition.

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Regular distribution from a locally integrable function

Definition

For open ΩRn, write fLloc1(Ω) when f:ΩC is Lebesgue measurable and Kf< for every compact KΩ. This extends A locally integrable function on Rn: equivalently, require integrability on every ball whose closure is a compact subset of Ω. Such balls finitely cover each compact K, and conversely their closures are compact; on all of Rn, any ball lies in a larger compact closed ball.

For a test φ as in Test function space d of an open set, define the regular functional

uf,φ=Ωf(x)φ(x)dx.

This is well-defined since fφsupKφKf for K=suppφ. It is complex-linear in f and in φ and depends only on the almost-everywhere class of f. For an empty support the integral is zero. The subsequent embedding theorem establishes continuity, so that this is a distribution, and injectivity modulo almost-everywhere equality. That theorem states the Countable Choice cost of its injectivity proof; no choice is needed to define the displayed pairing.

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Convolution of a distribution with a test function

Definition

For an integer n1, uD(Rn) as in Distribution, and φD(Rn), define

(uφ)(x)=u(y),φ(xy).

The variable y is the distribution variable: for fixed x, the smooth test yφ(xy) has compact support xsuppφ. The pairing is complex bilinear, with no conjugation; the displayed test, not the pairing convention itself, includes reflection of φ.

More generally, if uD(Ω), the same formula is defined on the open set {x:xsuppφΩ}. Openness follows by covering the compact set xsuppφ by finitely many balls whose closures lie in Ω, giving a positive translation margin. If φ=0, its support is empty and the formula defines zero everywhere. The subsequent smoothness theorem proves differentiability and its signs; the definition only pairs legitimate compactly supported tests.

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Test function cutoffs and euclidean localization

Statement

In ZF, for compact KΩRn with Ω open, there is χCc(Ω) with 0χ1 and χ=1 on a neighborhood of K. Every open cover of Ω admits an at most countable locally finite smooth partition of unity with compact supports, each support contained in some cover member. Subordination here asserts existence of such a member for each support; it does not select cover labels.

Facts & Assumptions

[F1]

The explicit smooth cutoff b equals one on the closed unit ball, vanishes outside the radius-two ball, and satisfies 0b1; translated dilates have the stated derivative scaling (Explicit compactly supported smooth cutoffs).

[F2]

Smoothness and multi-index notation are as in Ck maps and multi-index derivative notation in Euclidean space, applied componentwise.

Proof

Given: the compact set and open set of the first assertion; an open cover U of Ω for the second.

1.1

For each point of K, there is a rational center q and positive rational r such that the point belongs to B(q,r) and B(q,2r)Ω. These inner balls cover K, so a finite list suffices by compactness. Put bj(x)=b((xqj)/rj) and χ=1j(1bj). Then χ is smooth, lies in [0,1], equals one on the union of the inner balls, and has support in the finite union of the compact outer balls inside Ω. For K= take the empty product and χ=0.

givenF1F2
2.1

For nonempty Ω, set Kj={x:xj, dist(x,RnΩ)1/j} for j1, interpreting distance to the empty set as infinity; set K0=K1=. These are compact subsets of Ω, KjintKj+1, and their interiors cover Ω. Closedness follows from continuity of distance (its absolute difference is at most the distance of the two points), and boundedness gives compactness. The shell Hj=KjintKj1 is compact and lies in the open set Gj=intKj+1Kj2.

step 1.1algebra
3.1

Fix an enumeration of rational center/radius pairs and a coding of finite lists by natural numbers. For each j, consider pairs with B(q,2r)Gj and with this closed ball contained in some UU. Their inner balls cover Hj: at any shell point openness of Gj and of one cover member gives a sufficiently small ball, then a rational center and radius. Compactness gives a finite subcover. Select the least code of a finite list that covers Hj, taking the empty list for an empty shell. This is a specified function of j, not Countable Choice.

step 2.1given
4.1

Form the corresponding translated dilates bj,k of F1. Their supports lie in Gj, and each support lies in some cover member. This family is locally finite: a point has a neighborhood inside some KN, and supports with jN+2 miss KN, while only finitely many balls occur at each of the finitely many earlier stages. Every point belongs to some shell, so s=j,kbj,k is everywhere positive. The sum is locally finite and smooth; hence ηj,k=bj,k/s are smooth, nonnegative, have compact support in the same outer balls, and sum to one. The double index is countable. For empty Ω use the empty family. These constructions choose no cover labels and no arbitrary sequence of witnesses.

step 3.1F1F2
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Fixed support test function spaces are complete

Statement

For every compact KΩ with ΩRn open, the space DK is Hausdorff, locally convex and complete for d(f,g)=m=02m1min(1,pm(fg)). This metric induces exactly its derivative-seminorm topology. Multiplying the metric by two gives the equivalent convention with weights 2m. These assertions require no choice axiom.

Facts & Assumptions

[F1]

The functions, increasing seminorms and zero extensions are defined in Fixed support test function frechet space.

[F2]

On a nondegenerate closed real interval, uniform convergence of continuously differentiable functions and their derivatives identifies the derivative of the limit; the weaker hypothesis of convergence at one point suffices (If continuously differentiable functions converge at one point and their derivatives converge uniformly on a closed interval, then the functions converge uniformly to a differentiable function whose derivative is the derivative limit). Apply this to real and imaginary parts separately.

Proof

Given: a compact K and its space in F1.

1.1

Nonnegativity, symmetry and separation for d follow from F1 and its p0 term. The inequality min(1,a+b)min(1,a)+min(1,b) gives the triangle inequality term by term. For fixed m and 0<ε<1, d(f,g)<2m1ε implies pm(fg)<ε. Conversely, given ε>0, choose M with m>M2m1<ε/2; then pM(fg)<ε/2 gives d(f,g)<ε. These bounds identify the two topologies and their Cauchy sequences. Seminorm balls are convex, and their triangle and homogeneity inequalities give continuity of vector operations.

givenF1algebra
2.1

Let (fj) be d-Cauchy and extend each function smoothly by zero to Rn. For every multi-index α, the functions αfj are uniformly Cauchy on all of Rn: outside K they vanish and on K the bound is pα(fjfk). At each point their complex values have a unique limit gα(x). Passing k to infinity in the uniform Cauchy bound proves uniform convergence to gα. This definition uses unique limits, not a choice of subsequences. Each gα is continuous: at a point, approximate it uniformly by one continuous derivative within ε/3 and use continuity of that derivative. It vanishes off K.

step 1.1F1
3.1

Fix a coordinate direction ei, a point x and a positive h. On [h,h], the functions tαfj(x+tei) and their derivatives converge uniformly to gα(x+tei) and gα+ei(x+tei) respectively. F2, componentwise, gives igα(x)=gα+ei(x). All these functions are continuous by step 2.1, so iterating this identity shows g0 is smooth with every derivative gα. Its support is contained in the closed set K, hence g0ΩDK.

step 2.1F2
4.1

Uniform convergence of the finitely many derivatives of order at most m gives pm(fjg0)0 for each m, hence d(fj,g0)0 by step 1.1. This proves completeness. If K is empty or has empty interior, F1 makes the space zero and the same argument yields its sole element. No endpoint differentiation in Ω was assumed: the coordinate segments in step 3.1 lie in the globally smooth zero extension.

step 3.1step 2.1step 1.1F1
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Test function lf topology universal property

Statement

The test-function topology exists as a Hausdorff locally convex vector topology, induces the prescribed topology on each DK, and is the finest locally convex topology making all inclusions DKD(Ω) continuous. A linear map L:D(Ω)E to a locally convex space is continuous if and only if each restriction LDK is continuous. An increasing compact exhaustion whose interiors cover Ω gives the same topology, independently of the exhaustion. Thus this is an LF topology of the complete metrizable fixed-support spaces. All these claims hold in ZF.

Facts & Assumptions

[F1]

The topology uses all seminorms continuous on each fixed-support space; locally convex spaces here have topologies generated by seminorms (Test function topology).

[F2]

Each fixed-support space has its derivative-seminorm topology and is complete metrizable (Fixed support test function spaces are complete).

Proof

Given: Ω, its fixed-support spaces and the family P of F1.

1.1

Finite intersections of translated seminorm balls form a topology: each ball is stable under sufficiently small translates, by q(f)q(g)q(fg), and intersections refine intersections. Addition is continuous since q(h+k)q(h)+q(k). Joint scalar multiplication is continuous at (a,f) since q(bgaf)bq(gf)+baq(f). Near a, bound b by a+1 and make the two terms small for each of finitely many seminorms. Balls at zero are convex and balanced by the seminorm inequalities. Thus the construction is a locally convex vector topology.

givenF1algebra
2.1

Each inclusion is continuous by the definition of P. Define Qm(f)=maxαmsupxΩαf(x), with value zero on the empty domain. Compact support makes this finite and its restriction to DK is pm, so QmP. In particular Q0 separates points: if fg, the disjoint balls of radius Q0(fg)/3 about them separate them. On DK the induced topology is no finer than its prescribed topology by inclusion continuity, and no coarser because all pm are restrictions of Qm.

step 1.1F1F2
3.1

Let L be linear. If L is continuous, composing with each continuous inclusion proves continuity of every restriction. Conversely suppose all restrictions are continuous. For each seminorm r generating the topology of E, rL is a seminorm whose fixed-support restrictions are continuous. Thus rLP, and inverse images of translated finite seminorm balls are open, proving continuity of L. Applying this result to the identity into any other locally convex topology with continuous inclusions shows that topology is contained in the constructed one.

step 2.1F1
4.1

Let (Kj) be increasing compact sets with interiors covering Ω. Every compact KΩ is contained in some KN, by a finite subcover of these interiors and the maximum of its indices. The inclusion DKDKN is continuous since every derivative seminorm restricts to the same seminorm. Hence a seminorm continuous on all DKj is continuous on every DK, and the converse is immediate. The defining family of seminorms is therefore identical for any such exhaustion; step 3.1 also gives the stated map criterion using only exhaustion stages. By F2 these stages are Fréchet spaces. For Ω= they and the limit are zero, and the same statements hold. No witnesses are selected for infinitely many compact sets: the containment argument is for one fixed K at a time.

step 3.1F1F2
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Test function operations are continuous

Statement

In ZF the following linear maps are continuous for the LF test-function topologies: α:D(Ω)D(Ω); multiplication by a fixed aC(Ω); translation Thφ(x)=φ(xh) from D(Ω) to D(Ω+h); and composition CFφ=φF from D(V) to D(U) for a smooth diffeomorphism F:UV. Here smooth complex functions mean componentwise smooth real and imaginary parts. The translation and composition maps are topological isomorphisms. No joint continuity in varying a or h is asserted here.

Facts & Assumptions

[F1]

A linear map out of D is continuous exactly when its restrictions to all DK are continuous; fixed-support inclusions are continuous (Test function lf topology universal property).

[F2]

Ordered partial derivatives and smoothness have the conventions of Ck maps and multi-index derivative notation in Euclidean space.

[F3]

The total chain rule holds for differentiable Euclidean maps (The chain rule for total derivatives: D(gf)(a)=Dg(f(a))Df(a)).

[F4]

Proof

Given: the maps and domains of the statement, and a fixed compact source support K.

1.1

Differentiation does not enlarge support, and F4 gives pm(αφ)pm+α(φ). Thus it is continuous on each fixed-support space. For products the one-coordinate product rule follows by subtracting a(x)φ(x) from a(x+tei)φ(x+tei), inserting a(x+tei)φ(x) and dividing by t; continuity and the derivative limits give the two terms. Induction using F4 and Pascal's identity then gives [given, F2, F4, algebra] β(aφ)=γβ(βγ)(γa)(βγφ). All derivatives of a through order m are bounded on compact K, so pm(aφ)2mAm,Kpm(φ), where Am,K=maxγmsupKγa. Support again stays in K.

givenF2F4algebra
2.1

Translation takes support into K+h and preserves each derivative supremum. Its inverse is Th. Composition takes support into L=F1(K), compact because it is the image of K under the continuous inverse. The first derivative formula is i(φF)=j(jφ)FiFj, by F3. Inductively, each derivative of order m is a finite sum of (βφ)F, β, times products of derivatives of F of orders at most : differentiating a term either differentiates its coefficient by the product rule of step 1.1 or raises β by a coordinate using the first derivative formula. All coefficients are bounded on L, so pm,L(φF)Cm,K,Fpm,K(φ) for a finite constant.

step 1.1F2F3F4
3.1

The bounds in steps 1.1 and 2.1 prove continuity from each source stage into the indicated target stage. Composing with its continuous inclusion and applying F1 proves all asserted LF continuities. Apply the composition argument to F1 and the translation argument to h for continuous inverses. Empty support gives the zero test and all bounds hold with zero left side; the zero multi-index gives the identity map. Only finitely many derivative bounds are used for each estimate, so no choice axiom is needed.

step 1.1step 2.1F1
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Local finite order characterization of distributions

Statement

A complex-linear functional u:D(Ω)C is a distribution if and only if, for every compact KΩ, there are an integer mK0 and a finite constant CK0 such that u(φ)CKpmK(φ)(φDK). The quantifiers are K(mK,CK); no simultaneous selection of witnesses is asserted or needed. The equivalence holds in ZF.

Facts & Assumptions

[F1]

A distribution is a continuous complex-linear functional on the LF test space (Distribution).

[F2]

The LF universal property tests continuity of linear maps on every fixed-support space, whose topology is the derivative-seminorm topology (Test function lf topology universal property).

Proof

Given: a complex-linear functional u.

1.1

Suppose u is continuous, and fix K. By F2 its restriction is continuous at zero. Thus there are m0 and ε>0 such that pm(ψ)<ε implies u(ψ)<1: take the largest order and the smallest positive radius in a finite basic neighborhood contained in the inverse image of the open unit disk. If that intersection has no constraints, the whole space maps into the disk; linearity then makes u zero on this stage, and m=0,ε=1 works.

givenF1F2
2.1

For pm(φ)>0, apply step 1.1 to εφ/(2pm(φ)) to get u(φ)(2/ε)pm(φ). If pm(φ)=0, every positive real multiple of φ satisfies the same strict neighborhood inequality, so tu(φ)<1 for every t>0, forcing u(φ)=0. This proves the estimate for the fixed K, and the argument applies to every K without selecting a family of pairs.

step 1.1algebra
3.1

Conversely suppose the stated estimates hold. Fix K and one witnessing pair. If CK=0, the restriction is zero. Otherwise, for any δ>0, the neighborhood pmK<δ/CK maps into z<δ. Hence every restriction is continuous; F2 implies u is continuous on D(Ω), and F1 makes it a distribution. For empty K the space is zero and CK=mK=0 suffice.

step 2.1givenF1F2
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Order of a distribution on a compact set

Definition

For uD(Ω) and compact KΩ, the order of u on K is the least integer m0 for which there exists C0 satisfying u(φ)Cpm(φ) for all φDK. The set of such integers is nonempty by Local finite order characterization of distributions, so its least element exists without choice. If u vanishes on DK, including the empty or empty-interior compact cases, its order on K is assigned to be zero.

The distribution has global finite order if there is one integer m0 such that for every compact KΩ there is a finite CK0 with this bound of order m. The exponent is uniform; the constants need not be. Its global order is the least such exponent when one exists, and is infinity otherwise. The zero distribution has global order zero. Compactwise finite order by itself does not assert a uniform exponent over all compacts. An order-zero bound controls test values; it does not by definition identify the distribution with a function or a measure.

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Distributional derivative

Definition

For a distribution uD(Ω) in the convention of Distribution and a multi-index αN0n, its distributional derivative is αu,φ=(1)αu,αφ(φD(Ω)). The test derivative is a continuous linear endomorphism by Test function operations are continuous. Its composition with u, multiplied by the indicated sign, is therefore a continuous complex-linear functional, so this definition produces a distribution. The pairing is bilinear, with no complex conjugation. For α=0 the operation is the identity; for one coordinate derivative its sign is minus. Every derivative of the zero distribution is zero, including on the empty domain. No choice axiom enters this construction. Compatibility with classical derivatives requires an integration-by-parts argument where it is used; it is not part of the definition.

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Multiplication of a distribution by a smooth function

Definition

For uD(Ω) and a fixed smooth complex function aC(Ω) define au,φ=u,aφ(φD(Ω)). Multiplication of tests by a preserves compact support and is continuous and linear by Test function operations are continuous. Composing it with the continuous linear functional of Distribution shows auD(Ω). There is no conjugation of a in this bilinear convention.

The formulas give 1u=u, 0u=0, a0=0 and a(bu)=(ab)u by evaluation on every test; addition is distributive in either variable for the same reason. On the empty domain the construction gives the zero distribution. It requires no choice axiom. This operation is defined for a smooth multiplier; it does not define a product of two arbitrary distributions.

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Leibniz rule for distributions

Statement

For aC(Ω), uD(Ω) and αN0n, α(au)=βα(αβ)(βa)αβu. Here βα is coordinatewise and (αβ)=i(αiβi). The identity is in the bilinear complex convention and requires no choice axiom.

Facts & Assumptions

[F1]

Distribution derivatives are signed transposes of the continuous test derivatives, whose smooth mixed partials commute (Distributional derivative).

[F2]

Smooth multiplication is defined by (av)(φ)=v(aφ) and is associative (Multiplication of a distribution by a smooth function).

Proof

Given: a,u,α as in the statement.

1.1

Fix i and a test φ. The ordinary product rule gives aiφ=i(aφ)(ia)φ: subtract the product at a point from the product at its coordinate increment, insert the mixed product, divide by the increment and pass to the limit. Therefore [given, F1, F2, algebra] i(au),φ=u,aiφ=iu,aφ+u,(ia)φ. By F2 this is the first-order formula.

givenF1F2algebra
2.1

From F1, evaluating two consecutive derivative operations on a test gives the sign (1)γ+1 times u(γiφ). Commutation of the smooth test partials identifies this with (γ+eiu)(φ). Hence iγu=γ+eiu. The asserted formula for α=0 is au=au.

step 1.1F1
3.1

Suppose the formula holds for α. Differentiate it by i and apply step 1.1 to each smooth coefficient times its distribution. Step 2.1 yields the two terms with indices β+ei and β, respectively. For each resulting index η, their coefficients add to (αηei)+(αη)=(α+eiη), using zero for an out-of-range binomial coefficient. The equality is Pascal's identity in coordinate i with all other coordinate factors unchanged. Thus the formula holds for α+ei, and induction on total degree proves every case. All sums are finite. For a=0 or u=0 every term is zero, and on the empty domain the identity is between zero functionals.

step 2.1step 1.1F1F2algebra
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Dirac delta and its derivatives

Definition

For aΩ, the Dirac distribution at a is the complex-linear functional δa(φ)=φ(a) on D(Ω), with the bilinear convention of Distribution. It is continuous: the seminorm q(φ)=φ(a) restricts on each DK to a seminorm bounded by p0 (and is zero if aK), so it is one of the admissible seminorms defining the test topology.

Its derivatives are those of Distributional derivative: αδa,φ=(1)ααφ(a). They are distributions by that definition; on each fixed-support stage the absolute value is at most pα(φ). If aK all derivatives of the test vanish at a, so the bound has zero left side. For α=0 this is δa; a first derivative evaluates the negative first derivative of the test. No conjugation and no choice are involved. On the empty open set there is no permitted point a, rather than a new Dirac distribution. The sharp order of these distributions requires a test witness and is proved in the assigned example later.

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Distributions form a sheaf

Statement

For an open inclusion VΩ, restriction of a distribution is defined by testing on the smooth zero extension of a test in D(V). These restrictions are distributions and compose as restrictions do for functions. For any open cover (Uj)jJ of Ω, distributions ujD(Uj) agreeing on every overlap glue to a unique uD(Ω). This holds in ZF for an arbitrary index set J.

Facts & Assumptions

[F1]

Distributions are complex-linear continuous test functionals (Distribution).

[F2]

Continuity is equivalent to a finite-order estimate on each fixed compact support (Local finite order characterization of distributions).

[F3]

Multiplication by a smooth function preserves tests; its derivative estimates follow from the finite product rule used to justify Multiplication of a distribution by a smooth function.

[F4]

Every open cover has an at most countable locally finite smooth partition with compact supports, each support contained in some cover member, without selecting labels (Test function cutoffs and euclidean localization).

Proof

Given: the cover and compatible family in the statement.

1.1

For VΩ and φD(V), its support is compactly inside V, so extension by zero is smooth on Ω. On each compact KV its derivative seminorms are unchanged. The bound of F2 for u on K therefore gives that bound for its restriction; this proves restriction is a distribution. Testing successive zero extensions proves composition and identity of restrictions.

givenF1F2
2.1

Take the partition (η) of F4. For each and each test φ, the test ηφ has compact support inside any member containing suppη. Its evaluation by that member's distribution is independent of the member: two such members overlap on its support, so compatibility applies. Denote this uniquely specified number by v(φ); this definition selects no labels. Set u(φ)=v(φ). Only finitely many partition supports meet suppφ: local finiteness provides neighborhoods meeting finitely many supports, and a finite subcover of the compact support suffices. Thus the sum exists. Applying the same finite set to the union of two test supports proves complex linearity.

step 1.1givenF4
3.1

Fix compact KΩ. Only finitely many partition supports meet K. For these finitely many indices take containing cover members and their finite-order bounds on the compact supports of the corresponding η. Finite choices are provable by finite induction in ZF. Let m be the maximum of their orders, or zero if none occur. The finite product rule gives [step 2.1, F2, F3] pm(ηφ)2mmaxγmsupsuppηγη pm(φ)(φDK), where derivatives of φ vanish off K. Summing the finitely many bounds yields u(φ)CKpm(φ) with finite CK. Hence u is a distribution by F2.

step 2.1F2F3
4.1

If φD(Uj), every nonzero summand is evaluated on a test with compact support in Uj and a containing cover member. Compatibility makes it uj(ηφ). The finite sum is uj(φ) since η=1. Thus the restrictions are the prescribed ones. If a distribution w restricts to zero on each Uj, the same finite decomposition gives w(φ)=w(ηφ)=0 for every test. Applying this to the difference of two glued distributions proves uniqueness, and therefore independence of the partition. For the empty cover of the empty domain the sum defines the zero distribution and uniqueness still holds.

step 3.1step 2.1step 1.1F1F4
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Support of a distribution

Definition

For uD(Ω), say u vanishes on an open VΩ if its restriction to V is zero. Let Z be the union of all such open sets. Locality in Distributions form a sheaf shows that uZ=0, since these sets cover Z and all its restrictions are zero. Thus Z is the largest vanishing open set. The support is the relatively closed set suppu=ΩZ.

In particular suppu= if and only if u=0: emptiness gives Z=Ω and vanishing there, while the zero distribution vanishes everywhere. If a test φ vanishes on a neighborhood of suppu, its compact support is contained in Z, so u(φ)=0 by restriction. Consequently two tests agreeing on a neighborhood of suppu have equal pairings with u, by applying this fact to their difference.

Compactly supported means that suppu is a compact subset of Ω. Relative closedness in an arbitrary open domain is not by itself compactness or closedness in the ambient Euclidean space. On the empty domain support is empty. The union and locality argument require no selection of vanishing neighborhoods and no choice axiom.

TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Compactly supported distributions have global finite order

Statement

If uD(Ω) has compact support S, there are a compact neighborhood LΩ of S, an integer m0 and C0 such that, for every φD(Ω), u(φ)CmaxαmsupxLαφ(x). In particular one order exponent works on every fixed-support stage. The estimate is on a compact neighborhood, not necessarily on S itself. The result holds in ZF.

Facts & Assumptions

[F1]

Every distribution has a finite-order bound on each fixed compact support (Local finite order characterization of distributions).

[F2]

Tests agreeing near the support of a distribution have equal pairings; empty support means zero distribution (Support of a distribution).

[F3]

A compact subset of an open Euclidean set has a smooth compactly supported cutoff equal to one near it (Test function cutoffs and euclidean localization).

Proof

Given: u with compact support SΩ.

1.1

If S=, F2 gives u=0 and take L=, m=C=0, with the empty supremum zero. Otherwise take χ from F3 equal to one near S and put L=suppχ. It is compactly inside Ω and contains a neighborhood of S. For every test φ, the test (1χ)φ vanishes near S, so u(φ)=u(χφ) by F2.

givenF2F3
2.1

Apply F1 on the single compact L to obtain C0,m with u(ψ)C0pm(ψ) on DL. The finite product rule gives α(χφ)=βα(αβ)βχαβφ; this follows by iterating the coordinate product rule, with coefficients combined by Pascal's identity. If A=maxβmsupLβχ, then pm(χφ)2mAmaxγmsupLγφ. The derivatives of χ are bounded on L, so A is finite. Together with step 1.1 this gives the asserted estimate with C=C02mA.

step 1.1F1algebra
3.1

If φDK for another compact K, all its derivatives vanish off K, so the maximum over L is at most pm,K(φ). Thus the same exponent gives the global finite-order property (indeed the same C works here). The proof selected only one cutoff and one finite-order witness pair and used no choice axiom.

step 2.1
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Distributions supported at one point

Statement

For aΩ and uD(Ω), suppu{a} if and only if u=αmcααδa for some finite m0 and complex coefficients. The coefficients, with absent higher terms interpreted as zero, are unique. A nonzero such combination has support exactly {a}; the zero combination has empty support. This holds in ZF.

Facts & Assumptions

[F1]

Dirac derivatives evaluate tests by αδa(φ)=(1)ααφ(a) (Dirac delta and its derivatives).

[F2]

Compact support gives one finite-order estimate on a compact neighborhood for every test (Compactly supported distributions have global finite order). Two tests that agree on a neighborhood of the distributional support have equal pairings, and empty support is equivalent to the zero distribution (Support of a distribution).

[F3]

Smooth compact cutoffs equal to one near a prescribed compact set exist in ZF (Test function cutoffs and euclidean localization).

[F4]

On an open convex neighborhood, the Taylor remainder for a Ck function is o(hk) for k1 (Multivariable Taylor formula with o(hk) remainder). Apply separately to real and imaginary parts; for degree zero use continuity directly.

Proof

Given: aΩ and a distribution u.

1.1

Suppose its support is contained in {a}. Obtain an order m and constant from F2. Take a fixed smooth χ equal to one near zero with support in a ball B(0,R), using F3 in Euclidean space. For all sufficiently small ε>0, χε(x)=χ((xa)/ε) is supported inside Ω and equals one near a. Hence u(φ)=u(χεφ) for every test.

givenF2F3
2.1

Suppose γφ(a)=0 for every γm. For fixed γ, Taylor's formula F4 on a ball about a applied to γφ gives γφ(a+h)=o(hmγ); when γ=m this is continuity with value zero. The little-o bounds are uniform over hRε by their definition: their suprema are o(εmγ). The product rule expands a derivative of order β, βm, of χεφ into finitely many terms [step 1.1, F2, F4, algebra] (βγ)εβγ(βγχ)((xa)/ε)γφ(x),γβ. Each has supremum o(εmβ), hence tends to zero, even when β=m. All derivatives vanish outside the shrinking support. Thus F2 and step 1.1 give u(φ)Cmaxβmsupβ(χεφ)0, so u(φ)=0.

step 1.1F2F4algebra
3.1

Take a fixed cutoff η equal to one near a and compactly supported in Ω. For αm put qα(x)=η(x)(xa)α/α!. Direct monomial differentiation gives βqα(a)=1 for β=α and zero for every other βm. Therefore φαmαφ(a)qα has zero jet through degree m. Step 2.1 makes its pairing zero, giving u(φ)=u(qα)αφ(a). By F1 this is the claimed representation with cα=(1)αu(qα).

step 2.1F1F3
4.1

Conversely, every test supported in Ω{a} has all derivatives zero at a, so F1 makes every displayed combination vanish there. Its support is therefore contained in {a}. Evaluate a zero combination on the tests qα constructed with the largest order occurring to see each coefficient is zero. This proves uniqueness. Finally a nonzero distribution cannot have empty support by locality (as used in F2), so a nonzero combination has support exactly {a}. The zero combination is allowed with m=0,c0=0. All jets and sums are finite and no choice is used.

step 3.1F1F2
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Bounded test function sets have common compact support

Statement

A set BD(Ω) is bounded (absorbed by every zero-neighborhood) if and only if there is compact KΩ containing every support of its members and supfBpm(f)< for each m0. A convergent sequence together with its limit is bounded. These assertions hold in ZF; no sequence of witnesses escaping compact sets is selected.

Facts & Assumptions

[F1]

The LF topology is generated by all seminorms continuous on each fixed-support stage. Its global derivative suprema Qm restrict to pm, and stage inclusions are continuous (Test function lf topology universal property).

Proof

Given: BD(Ω).

1.1

In a seminorm-generated topology, boundedness is equivalent to supBq< for every defining seminorm q. Indeed absorption by q<1 bounds q on B. Conversely, for finitely many constraints qi<εi, choose t>maxi(supBqi/εi), with t=1 if there are no constraints, to absorb B in their intersection. By F1, a bounded B therefore satisfies supBQm< for every m.

givenF1algebra
2.1

Assume B bounded and take the explicit compact exhaustion Kj={x:xj, dist(x,RnΩ)1/j} for j1, with distance to the empty set infinity and K0=. These closed bounded sets are compactly inside Ω, satisfy KjintKj+1, and their interiors cover Ω. Put Aj=KjKj1. The disjoint sets Aj cover Ω, and each compact subset meets only finitely many of them, by a finite subcover from the interiors. Define aj=sup{f(x):fB,xAj}, with empty supremum zero. Step 1.1 bounds every aj by the finite number supBQ0.

step 1.1F1
3.1

Set w(x)=j/aj for xAj when aj>0, and zero on shells with aj=0. This finite nonnegative function is bounded on every compact subset, since only finitely many shells meet that subset. Continuity of w is unnecessary. The seminorm q(f)=supxΩw(x)f(x) is finite for each test and restricts to a seminorm bounded by supKwp0 on each DK, hence belongs to the defining family of F1. For any occupied shell (aj>0), supfBq(f)(j/aj)aj=j by the supremum definition. If no KN contains all supports, for every N some member has a nonzero value outside KN (otherwise its support, the closure of nonzero values, would lie in the closed set KN). Thus occupied indices are unbounded, forcing supBq=, contrary to step 1.1. A common compact KN must exist, and its derivative bounds are the Qm bounds already obtained.

step 2.1step 1.1F1
4.1

Conversely suppose the stated common support and derivative bounds hold. On DK, any defining seminorm q is continuous. Its unit ball contains pm<ε for some m,ε>0; scaling as in step 1.1 gives q(f)(2/ε)pm(f), with the zero-seminorm case handled by arbitrarily large scaling. Hence q is bounded on B, and step 1.1 proves LF boundedness. Finally, if fjf, continuity of each seminorm gives q(fjf)0, so {q(f),q(fj):j1} is bounded by a finite initial maximum and a bounded tail. Step 1.1 proves boundedness of the sequence and its limit. Empty B uses K= and zero suprema; empty Ω has only the zero test. The weights in step 3.1 are defined from suprema, without any countable choice.

step 3.1step 1.1F1
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Sequential convergence in test function space

Statement

For tests φj,φD(Ω), φjφ in the LF topology if and only if the supports of φj eventually lie in one compact KΩ and αφjαφ uniformly on Ω for every multi-index α. Equivalently one compact contains all their supports and that of the limit, and convergence holds in its fixed-support topology. This is a sequence criterion, in ZF.

Facts & Assumptions

[F1]

The LF topology induces exactly the derivative-seminorm topology on each fixed-support space, and its inclusion is continuous (Test function lf topology universal property).

[F2]

A convergent sequence with its limit is bounded; a bounded set of tests has common compact support (Bounded test function sets have common compact support).

Proof

Given: a sequence of tests and a test φ.

1.1

If φjφ in the LF topology, F2 places the sequence and limit in one DK. Convergence in the subspace topology follows directly: any subspace neighborhood of φ is the intersection with an ambient neighborhood, which eventually contains the sequence. By F1, pm(φjφ)0 for every m. Since derivatives vanish off K, this gives uniform convergence of every derivative on Ω.

givenF1F2
2.1

Conversely suppose eventual common support and the stated uniform convergence. For xK, the eventual values φj(x) are zero, so φ(x)=0; since K is closed, its support is contained in K. The finite union L of K and the finitely many initial test supports is compactly inside Ω and contains every support. For each m, uniform convergence of the finitely many derivatives through order m gives pm,L(φjφ)0. F1 first yields convergence in DL, then LF convergence by the continuous inclusion.

step 1.1givenF1
3.1

These arguments also prove the stated equivalent all-support formulation. Empty Ω and eventually zero sequences satisfy the same reasoning; no compactness extraction or chosen subsequence is used. The finite initial union is essential to passing from eventual to all-support language.

step 2.1step 1.1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Weak and strong topologies on distributions

Definition

On the distribution space of Distribution, the weak distribution topology is generated by the seminorms uu(φ) for individual φD(Ω). It is also called the weak-star topology relative to the test space: σ(D,D).

The strong distribution topology, denoted β(D,D), is generated by pB(u)=supφBu(φ) for bounded subsets B of the LF test space. The supremum of the empty set is zero. This is finite: continuity of u gives a zero-neighborhood U with u(φ)<1 on U, and boundedness gives BtU for some finite t>0, so pB(u)t. Equivalently the bounded sets are exactly the common-compact-support, derivative-bounded sets characterized in Bounded test function sets have common compact support. Homogeneity and the triangle inequality for each pB follow by taking suprema of the corresponding inequalities for evaluations.

For a net (ui) and a distribution u, weak convergence means ui(φ)u(φ) for every test; strong convergence means pB(uiu)0 for every bounded B. These are precisely the convergence conditions in the generated topologies, since neighborhoods impose finitely many seminorm bounds and directedness gives a common eventual index. The same definitions apply to sequences, without identifying arbitrary net behavior with sequence behavior. Both topologies are Hausdorff: distinct functionals differ on some test, and singleton tests are bounded by the cited characterization. The empty domain gives the zero dual. No choice axiom is used.

TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Locally integrable functions embed in distributions

Statement

Assume the Axiom of Countable Choice. The map fuf, uf(φ)=Ωfφ, is a complex-linear injection from Lloc1(Ω) modulo almost-everywhere equality into D(Ω). It is continuous from the local L1 topology, generated by fKf for compact KΩ, to the strong distribution topology. In particular local L1 convergence implies strong distribution convergence. The continuity estimate itself is choice-free; Countable Choice is used in the cited L1 approximate-identity theorem proving injectivity.

Facts & Assumptions

[F1]

The regular functional is well-defined modulo almost-everywhere equality and satisfies uf(φ)Kfp0(φ) on DK (Regular distribution from a locally integrable function).

[F2]

Compactwise finite-order bounds characterize distributions (Local finite order characterization of distributions).

[F3]

A unit-mass smooth bump has rescalings ρε(x)=εnρ(x/ε) (The mollifier family generated by a unit-mass smooth bump); these form an L1 approximate identity under Countable Choice (A unit-mass smooth bump generates an L1 approximate identity).

[F4]

Under Countable Choice, convolution by such an approximate identity converges to each gL1(Rn) in L1 (Every L1 approximate identity converges to the identity in Lp for 1p<, with p=1).

[F5]

Smooth nonnegative compact cutoffs equal to one near a compact set exist (Test function cutoffs and euclidean localization).

[F6]

Strong seminorms are uniform pairings over bounded test sets, which have common compact support and bounded derivative suprema (Weak and strong topologies on distributions).

[F7]

Dominated convergence gives L1 convergence for almost-everywhere convergent functions under one integrable majorant (Dominated convergence).

[F8]

The assumed choice principle is The Axiom of Countable Choice (ACω).

Proof

Given: Countable Choice and a locally integrable function f on Ω.

1.1

F1 and F2 show uf is a distribution, with compactwise order at most zero; linearity of the integral gives linearity in the equivalence class of f. For a bounded test set B, take its common compact K and finite MB=supφBp0(φ). Then pB(uf)MBKf. Thus each strong seminorm of the image is bounded by a constant times a defining local L1 seminorm, proving continuity, including for nets. If MB=0, the left side is zero.

givenF1F2F6
2.1

Suppose uf=0, and fix a closed ball H compactly inside Ω. Take χ from F5 equal to one near H and compactly supported in Ω, and extend g=χf by zero to Rn; it is in L1 by local integrability. Also use F5 to obtain a nonnegative smooth compact bump equal to one on a ball, and divide it by its positive finite integral to obtain ρ of mass one. Fix R containing its support. For sufficiently small ε, every xH has xsuppρε inside the neighborhood where χ=1: the compact H has positive distance from that neighborhood's closed complement. Consequently [step 1.1, given, F3, F5] gρε(x)=Ωf(y)ρε(xy)dy=uf(ρε(x))=0. The integrals are absolutely finite because gL1 and the kernel is bounded; the reflected kernel is a test compactly supported in Ω.

step 1.1givenF3F5
3.1

By F3, F4 and F8, gρεg10. Since g=f on H and step 2.1 makes the convolution zero there, Hfgρεg10. Hence f=0 almost everywhere on H. The closed rational balls compactly inside Ω are countable and their interiors cover Ω; applying this conclusion to each and taking the countable union of the null sets {xH:f(x)0} gives f=0 almost everywhere on Ω. No family of cutoffs was selected simultaneously. Apply the same argument to fh for injectivity of the map on equivalence classes.

step 2.1F3F4F8
4.1

As a useful convergence consequence, if fjf almost everywhere and for every compact K there is an integrable majorant of all fj on K, F7 gives Kfjf0. Step 1.1 then gives strong convergence ufjuf. On the empty domain there is only the zero class and zero distribution, so injection and continuity still hold.

step 3.1step 1.1F7
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Strong distribution convergence implies weak convergence

Statement

If a net of distributions converges strongly, it converges weakly to the same distribution. In particular this holds for sequences. The implication requires no choice axiom.

Facts & Assumptions

[F1]

Strong convergence is convergence uniformly on every bounded test set; weak convergence is pointwise convergence on tests, and every singleton test set is bounded (Weak and strong topologies on distributions).

Proof

Given: uiu strongly.

1.1

Fix a test φ. By F1 the singleton B={φ} is bounded and ui(φ)u(φ)=pB(uiu)0.

givenF1
2.1

The test was arbitrary, so F1 identifies these scalar limits as weak convergence. This uses one given test at a time, without a simultaneous selection. For the zero test the seminorm is zero; on the empty domain the sole distribution is zero. No converse for arbitrary nets is asserted.

step 1.1F1
LemmaStatement: AI-adaptedProof: AI-adaptedaudited 2026-09-13Open item page →

Riemann–Lebesgue comparison for distribution test integrands

Statement

Assume Countable Choice. Let Q=i=1n[ai,bi] with n1 and ai<bi. If a bounded Borel real function f on Q is Riemann integrable, then it is Lebesgue integrable and the two integrals agree. The corresponding assertion for complex functions holds componentwise. In particular it applies to smooth compact test integrands and their bounded Borel zero extensions from compact Jordan regions.

Facts & Assumptions

[F1]

Darboux and tagged Riemann integrability agree, with the same value (The multidimensional Darboux and tagged-mesh definitions of the Riemann integral agree).

[F2]

Under Countable Choice, any set between the interior and closure of a box has its box volume as Lebesgue measure (A box in Rn with parameters aibi is Lebesgue measurable of measure i<n(biai), whichever of its faces are included).

[F3]

The nonnegative integral is monotone and homogeneous and agrees with the simple integral on nonnegative simple functions (Monotonicity and nonnegative homogeneity of the nonnegative integral, The nonnegative integral agrees with the simple integral on simple functions).

[F4]

Lebesgue integration is complex-linear on L1 (The Lebesgue integral is linear on L1(μ)).

[F5]

Assume The Axiom of Countable Choice (ACω), used for F2 and the Lebesgue-measure interface.

[F6]

A continuous real function on a nonempty compact metric space is bounded (A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value), and a compact subset of a metric space is closed (A compact subset of a metric space is closed and bounded). Riemann integration over a bounded Jordan set is defined by the zero extension to a bounding rectangle (The Riemann integral of a bounded function over a bounded Jordan measurable set), and a continuous real function on a compact Jordan set is integrable in that sense (A continuous real function on a compact Jordan measurable set is Riemann integrable over that set).

Proof

Given: the bounded Borel Riemann integrand and F5.

1.1

Choose C0 with fC, and put g=f+C0. F2 and F3 give QfCvolQ< and Qg2CvolQ<, so both are integrable. Borel measurability makes all these integrals defined.

givenF2F3F5
2.1

For a finite rectangular grid, list its closed cells Q1,,Qs and set Di=Qij<iQj. These Borel sets partition Q, contain each cell's interior and lie in its closure, so λn(Di)=volQi by F2. Put mi=infQig and Mi=supQig. The simple functions l=imi1Di and h=iMi1Di satisfy lgh everywhere, including grid faces. F3 identifies their integrals as the grid's lower and upper Darboux sums for g, so these sums bracket Qg.

step 1.1F2F3
3.1

Every tagged sum of g is the corresponding sum of f plus CvolQ. Thus g is Riemann integrable with value IR(f)+CvolQ. F1 says its supremum of lower Darboux sums and infimum of upper sums have this same value. Taking these bounds in step 2.1 squeezes Qg to that value. F4 and F2 then give Qf=QgCvolQ=IR(f).

step 2.1F1F2F4
4.1

Apply the real result separately to the real and imaginary parts for the complex assertion; the bound fRef+Imf ensures absolute integrability. For the last clause, let EQ be compact Jordan and let h:ER be continuous. If E is empty its zero extension is zero. Otherwise F6 makes h bounded, and compactness makes E closed. Its zero extension h~ is Borel: for every open OR, continuity in the subspace gives h1(O)=EG for some open GQ; then h~1(O)=EG when 0O, while h~1(O)=(QE)(EG) when 0O. The definition and theorem in F6 say exactly that this bounded zero extension is Riemann integrable on Q. The real result therefore applies; treating real and imaginary parts gives the same conclusion for continuous complex integrands, in particular smooth compact test integrands. The zero function has both integrals zero; the constant one has both integrals volQ. Degenerate boxes are excluded from this statement. No choices of tags over an infinite family were used; Countable Choice is precisely the measure hypothesis in F2.

step 3.1F1F2F4F5F6
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Distributional differentiation is continuous and commutes

Statement

Distributional derivatives satisfy αβu=α+βu and are continuous linear maps on D(Ω) for both the weak and strong distribution topologies. These claims hold in ZF. Assuming Countable Choice for the cited Riemann-to-Lebesgue comparison, if fCk(Ω;C) and αk, then αuf=uαf. In particular distributional differentiation extends classical smooth differentiation.

Facts & Assumptions

[F1]

Derivatives are signed transposes of test derivatives (Distributional derivative).

[F2]

Weak seminorms test one function, and strong seminorms test bounded sets (Weak and strong topologies on distributions).

[F3]

Test differentiation is continuous and preserves compact supports with pm(αφ)pm+α(φ) (Test function operations are continuous).

[F5]

Regular functionals pair by the bilinear Lebesgue integral (Regular distribution from a locally integrable function).

[F6]

Compact sets admit smooth compact cutoffs equal to one near them (Test function cutoffs and euclidean localization).

[F7]

One-dimensional integration by parts holds when the factors and their derivatives are continuous on a closed interval (If u,v are differentiable on [a,b] with u,v integrable, then abuv=u(b)v(b)u(a)v(a)abuv).

[F8]

For Riemann-integrable functions on boxes with integrable sections, iterated and multiple Riemann integrals agree (Riemann--Fubini on product rectangles, with lower and upper section integrals and content-zero exceptional sections).

[F9]

Under Countable Choice, a bounded Borel Riemann-integrable real function on a nondegenerate box has the same Lebesgue integral (Riemann–Lebesgue comparison for distribution test integrands).

[F10]

Countable Choice, needed only for the classical-compatibility clause below, is The Axiom of Countable Choice (ACω).

Proof

Given: a distribution u and multi-indices α,β.

1.1

Evaluating consecutive derivatives on a test gives (1)α+βu(βαφ). By F4 this is (1)α+βu(α+βφ), which is α+βu(φ) by F1. Linearity follows from the same formula.

givenF1F4
2.1

For a single test φ, the weak seminorm of αu equals u(αφ), a weak seminorm of u. For a bounded test set B, the set αB is bounded: any zero-neighborhood has a zero-neighborhood inverse image under the continuous linear test derivative of F3, and absorption of B by that inverse image gives absorption of its image. Hence pB(αu)=pαB(u), a strong seminorm. These equalities prove continuity in both topologies, including for nets, without choice.

step 1.1F1F2F3
3.1

Now assume F10 and fC1(Ω;C), and fix a test φ. Use F6 to choose χ=1 near suppφ with compact support in Ω. Extend g=χf by zero to Rn; it is C1 because it vanishes near the complement of Ω. Choose a nondegenerate box containing the supports of g and φ in its interior. On each coordinate segment F7 gives giφ=(ig)φ, because φ is zero at both endpoints. For complex functions expand into real and imaginary parts and apply the real identity to the four products. All integrands and sections are continuous on compact boxes and are Riemann integrable: uniform continuity makes their oscillation Darboux sums arbitrarily small on sufficiently fine uniform grids. For n>1, F8 integrates the one-coordinate identity over the remaining coordinates; for n=1 this is already the required identity. F9 componentwise converts the two multiple Riemann integrals to Lebesgue integrals. Near the test support g=f and ig=if, so the resulting equality is Ωfiφ=Ω(if)φ.

step 2.1F6F7F8F9F10
4.1

Continuous functions and their continuous derivatives are locally integrable by compact boundedness, and their regular functionals are continuous since their absolute pairings are bounded by Kfp0 on each fixed support (or directly by the global Q0 seminorm on that stage). F5 and step 3.1 therefore give iuf=uif. Iterating this identity through at most k derivatives for fCk, and using step 1.1, proves the classical-compatibility formula. Degree zero is the identity. Zero functions and the empty domain give zero distributions. The additional axiom enters only through the integral comparison in the classical argument; the algebra and dual continuity remain choice-free.

step 3.1step 1.1F1F5
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Distribution pairing with smooth parameter families

Statement

Let r,n1 be integers, let XRr and YRn be open, let uD(Y), and let FC(X×Y;C). Suppose that for each compact HX there is compact KY with suppyF(x,)K for every xH. Then a(x)=u(F(x,)) is smooth and xαa(x)=u(xαF(x,)).

Assume Countable Choice for the following integral clause. For every compact measurable EX, the function G(y)=EF(x,y)dx is a test in D(Y), its y derivatives pass under the integral, and u(G)=Eu(F(x,))dx. Integrals here are Lebesgue integrals. The smoothness and differentiation claims hold in ZF; Countable Choice supplies Lebesgue measure for the integral clause. Neither clause uses tensor products or distributional mollification.

Facts & Assumptions

[F1]

On each DK, u has a finite-order estimate (Local finite order characterization of distributions).

[F2]

Smooth test operations preserve compact support and commute as ordinary partial derivatives (Test function operations are continuous).

[F3]

Each DK is complete in its derivative-seminorm metric (Fixed support test function spaces are complete).

[F4]

A compact parameter set has a smooth compact cutoff equal to one near it (Test function cutoffs and euclidean localization).

[F5]

The mean-value inequality bounds the increment of a differentiable vector-valued curve by its length times a bound on its derivative; use C=R2 (The mean value inequality: if f:[a,b]Rm is continuous and differentiable on (a,b) with f2M, then f(b)f(a)2M(ba)).

[F6]

Absolute integrals bound moduli of integrals (The modulus of an integral is bounded by the integral of the modulus), and integration is complex-linear on L1 (The Lebesgue integral is linear on L1(μ)).

Proof

Given: integers r,n1, X,Y,u,F, and the compact-support hypothesis.

1.1

Around a fixed x0X take a closed ball with a slightly larger closed ball still inside X. The hypothesis on the larger ball supplies a single compact K for all its slices. Every parameter derivative of F has support in K for parameters in the smaller ball: for yK the function is identically zero for all parameters in a neighborhood, so all parameter derivatives vanish there. On the smaller ball times K, every mixed derivative is uniformly continuous by compactness. Thus pm(F(x,)F(x0,))0 for each m, and F1 gives continuity of a.

givenF1F2
2.1

Fix a coordinate i. For each βm, apply F5 on the segment from 0 to h (reverse its orientation if h<0) to the curve tyβF(x+tei,y)tiyβF(x,y). The derivative increment is bounded uniformly in yK by a modulus of continuity tending to zero with h. Dividing the resulting inequality by h proves [step 1.1, F1, F2, F5] pm(F(x+hei,)F(x,)hiF(x,))0. The F1 estimate passes this limit through u. Apply step 1.1 to the parameter derivative slices for continuity of the resulting derivative, and repeat for every multi-index. This proves smoothness and the derivative formula.

step 1.1F1F2F5
3.1

For this clause assume Countable Choice and use F7 for Lebesgue measure. Fix compact EX. By F4 choose a smooth cutoff θ=1 near E with compact parameter support in X. Extend F~=θF by zero to all parameter space. It is smooth, and the support hypothesis on suppθ gives a common compact K for all its slices and their y derivatives. Choose a closed box Q whose interior contains that parameter support. At level j divide each side into 2j equal pieces, disjointify the cells by assigning shared faces in coordinate order, and let tC be each cell's lower corner. Put [step 2.1, given, F2, F4, F6, F7] Sj(y)=Cλr(EC)F~(tC,y). These are tests supported in K. For every m, uniform continuity of the finitely many y derivatives through order m on Q×K supplies a modulus ωm(δ)0. For each derivative and fixed y, F6 bounds the error between the grid sum and its scalar integral over E by λr(E)ωm(meshj). The finite sum is exactly the integral of the corresponding step function, and its weights are finite because E lies in a bounded box.

step 2.1givenF2F4F6F7
4.1

Comparing two grid sums via their scalar integrals gives pm(SjSk)λr(E)(ωm(meshj)+ωm(meshk)). Thus F3 gives a limit SDK. The degree-zero scalar error in step 3.1 identifies S(y)=EF(x,y)dx=G(y), and its higher-degree errors identify every derivative of S with the corresponding integral. By F1, u(Sj)u(G). By finite linearity u(Sj)=Cλr(EC)u(F~(tC,)), which converges to Ea(x)dx by the same uniform-continuity integral estimate, since step 2.1 makes that scalar function smooth. This proves interchange. Empty or measure-zero E gives zero sums and integrals; empty Y gives zero slices. All tags and grids are specified, not chosen from an infinite family.

step 3.1step 2.1F1F3F6
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Convolution with a test function is smooth

Statement

Let uD(Ω) and φD(Rn). On the open safe domain V={x:xsuppφΩ}, the convolution uφ is smooth and α(uφ)=(αu)φ=u(αφ). The last expression is restricted to V if its own safe domain is larger. For Ω=Rn the domain is all of Rn. This holds in ZF.

Facts & Assumptions

[F1]

The convolution is uφ(x)=u(φ(x)) on its open safe domain (Convolution of a distribution with a test function).

[F2]

A smooth parameter family with locally common compact test support pairs smoothly with a distribution, and parameter derivatives pass through the pairing (Distribution pairing with smooth parameter families).

[F3]

Distribution derivatives act by signed test differentiation (Distributional derivative).

Proof

Given: u,φ,V as in the statement.

1.1

Put S=suppφ. For compact HV, all y-supports of F(x,y)=φ(xy), xH, lie in HS. This is compact as the continuous image of the compact product H×S, and it lies in Ω by the definition of V. The function F is jointly smooth, so F2 gives smoothness of uφ and xα(uφ)(x)=u((αφ)(x)).

givenF1F2
2.1

Differentiating the reflected test in y gives yαφ(xy)=(1)α(αφ)(xy). F3 therefore gives (αu)φ(x)=(1)αu(yαφ(x))=u((αφ)(x)), since the two signs multiply to one. Together with step 1.1 this proves both equalities.

step 1.1F1F3algebra
3.1

The support of αφ is contained in S, so its safe domain contains V, justifying the stated restriction. If φ=0, the safe domain is all of Rn and each expression is zero. If V is empty the smoothness and equalities are vacuous on that open set; α=0 gives the defining convolution identity. No choice is used.

step 2.1F1
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Uniform finite order bounds for pointwise bounded distributions

Statement

Assume Dependent Choice. If UD(Ω) is pointwise bounded, meaning supuUu(φ)< for every test φ, then for each compact KΩ there are m0,C0 with u(φ)Cpm(φ)(uU, φDK). Every pointwise limit of a net drawn from this family is a distribution. In particular the pointwise limit of any pointwise convergent sequence of distributions is a distribution. The pointwise-bounded-family hypothesis is not silently discarded for arbitrary nets.

Facts & Assumptions

[F1]

A linear functional with compactwise finite-order bounds is a distribution (Local finite order characterization of distributions).

[F2]

Each DK is complete metrizable with its increasing derivative seminorms (Fixed support test function spaces are complete).

[F3]

Under Dependent Choice, a nonempty complete metric space covered by countably many closed sets has one such set with nonempty interior (Under Dependent Choice, a nonempty complete metric space is not a countable union of closed sets with empty interior).

Proof

Given: Dependent Choice and a pointwise bounded family U.

1.1

Fix compact K. For integers j1 put Ej={φDK:u(φ)j for every uU}. It is closed as an intersection of inverse images of closed disks under continuous restrictions. Pointwise boundedness implies DK=j1Ej. The space contains zero, so it is nonempty, and F2–F4 give an Ej with nonempty interior.

givenF2F3F4
2.1

Take φ0 and a neighborhood φ0+{h:pm(h)<ε}Ej for some m0,ε>0. Then φ0Ej and for pm(h)<ε, linearity gives u(h)u(φ0+h)+u(φ0)2j for all u. For pm(h)>0, scale h by ε/(2pm(h)) to obtain u(h)(4j/ε)pm(h). If pm(h)=0, every positive multiple is in the neighborhood, forcing u(h)=0 by the same uniform bound. This gives the claimed estimate for the fixed K.

step 1.1algebra
3.1

Let a net from U converge pointwise to a scalar-valued map v on tests. Passing to limits in addition and scalar multiplication shows v is complex-linear. Passing to the limit in the bound from step 2.1 gives v(φ)Cpm(φ) on each DK. F1 proves v is a distribution. The witnesses are obtained for one compact at a time, with no additional choice principle.

step 2.1F1
4.1

For a pointwise convergent sequence, every scalar sequence of evaluations is bounded: its convergent tail is bounded and its remaining finite set has a finite maximum. Thus its range is a pointwise bounded family, and step 3.1 applies. A general convergent scalar net need not be bounded over all its indices, so that reasoning is used only for sequences. For empty U take C=m=0; empty K has zero test space and the same choice works. Dependent Choice was used precisely in F3 for the Baire step.

step 3.1step 2.1F3F4
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Pullback of a distribution by a diffeomorphism

Definition

Let n1 be an integer, let F:UV be a smooth diffeomorphism between open subsets of Rn, and let uD(V) in the convention of Distribution. Put JF(x)=detDF(x). The chain rule applied to F1F makes DF(x) invertible, so JF>0. It is smooth: the determinant is smooth and nonzero, and its sign is locally constant. Define Fu,φ=u,(φJF)F1(φD(U)). The transformed test has support in F(suppφ), a compact subset of V. Multiplication by the fixed smooth reciprocal Jacobian and diffeomorphic composition are continuous linear test operations by Test function operations are continuous, so their transpose defines a distribution. This definition is choice-free. For the identity map the Jacobian is one and the pullback is the identity; the zero distribution pulls back to zero. Empty diffeomorphic domains give zero test and distribution spaces. The absolute value of the determinant handles orientation reversal; arbitrary smooth maps are not covered by this definition.

For compatibility with functions, use the regular-functional convention of Regular distribution from a locally integrable function and assume the Axiom of Countable Choice (The Axiom of Countable Choice (ACω)). If fLloc1(V) and KU is compact, put hK=1F(K)f. This is an L1(V) function. The exact formula of A C^1 diffeomorphism satisfies the change-of-variables formula for L^1 functions gives Kf(F(x))JF(x)dx=F(K)f(y)dy<. The formula also makes the transformed integrand measurable; since JF is positive and its reciprocal is bounded on K, this proves fFLloc1(U). If f=g almost everywhere, apply the same formula to 1F(K)fg, whose integral is zero, and use A nonnegative measurable function has integral 0 exactly when it vanishes almost everywhere to obtain fF=gF almost everywhere on each compact K. Thus composition respects local almost-everywhere classes. For a fixed test φ, the function f(y)(φ/JF)(F1(y)) is integrable, since its smooth factor is bounded with compact support. Applying the same change-of-variables formula gives Fuf,φ=Vf(y)φ(F1(y))JF(F1(y))dy=Uf(F(x))φ(x)dx. Thus Fuf=ufF, with both regular functionals distributions by Locally integrable functions embed in distributions. Countable Choice is used through the published Lebesgue change-of-variables and embedding results, not through the definition of Fu.

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Tensor product of distributions

Definition

For uD(U), vD(V), where URp and VRq are open, define the tensor-product candidate on ΦD(U×V) by uv,Φ=u, xv,Φ(x,). The pairings are complex-bilinear as in Distribution. To check that the outer pairing is meaningful, let KU,KV be the compact coordinate projections of suppΦ. Every inner test is supported in KV, so Distribution pairing with smooth parameter families makes its pairing a smooth function of x. That function is zero off KU, since those slices are zero, so it is a test in D(U).

The candidate is complex-linear in Φ and separately linear in u,v by linearity of each pairing. For Φ(x,y)=φ(x)ψ(y) its value is u(φ)v(ψ). If either distribution is zero, the value is zero; if either open domain is empty, the test space on the product is zero. This construction uses no choice axiom. Continuity of the candidate on the product test space, uniqueness from product tests, and equality with the reversed iterated pairing are proved in the tensor-product theorem below; they are not inferred from the notation.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Compactly supported distributions extend to smooth functions

Statement

Give C(Ω) the topology generated by qK,m(f)=maxαmsupKαf for compact KΩ and m0. A compactly supported distribution u has a unique continuous complex-linear extension to this space, given by u~(f)=u(χf), where χD(Ω) is any cutoff equal to one near suppu. The result is independent of χ. The extension vanishes on every smooth function zero near the support. No Hahn–Banach or choice axiom is needed.

Facts & Assumptions

[F1]

A compactly supported distribution has a finite-order bound on one compact neighborhood for all tests (Compactly supported distributions have global finite order).

[F2]

Tests vanishing near the support have zero pairing; the empty-support distribution is zero (Support of a distribution).

[F3]

Compact subsets admit smooth compact cutoffs equal to one on a neighborhood (Test function cutoffs and euclidean localization).

Proof

Given: a compactly supported distribution u, with support S.

1.1

Take χ from F3. For any smooth f, χf is a test. If χ is another such cutoff, (χχ)f is a test vanishing near S, so F2 gives equal pairings. Thus the formula is well-defined, linear, and agrees with u on tests because (1χ)φ vanishes near S. If f vanishes near S, so does χf, proving the additional vanishing assertion.

givenF2F3
2.1

Apply F1 to χf. Let m,C,L be its fixed estimate and put L=Lsuppχ, compactly inside Ω. The finite product formula gives qL,m(χf)2mAqL,m(f), where A=maxβmsupLβχ<. Hence u~(f)C2mAqL,m(f), proving continuity in the stated smooth topology.

step 1.1F1algebra
3.1

Tests are dense in that topology. Indeed a basic neighborhood of a smooth f imposes finitely many derivative bounds on compacts K1,,Ks. Use F3 on their finite union to take η=1 near that union. Then the test ηf has exactly the same derivatives as f on all these compacts, so it lies in the neighborhood. If there are no constraints, the zero test suffices. If two continuous linear extensions agree on tests, their difference has a closed zero kernel (the scalar target is Hausdorff) containing this dense set, hence vanishes on all smooth functions. This proves uniqueness without selecting a sequence of cutoffs. For empty S take the zero extension; empty Ω has only the zero function.

step 2.1F2F3
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Sequential convergence of smooth multipliers and distributions

Statement

Assume Dependent Choice. If uju weakly in D(Ω) and aj,aC(Ω) satisfy uniform convergence of every derivative on every compact set, then ajujau weakly. Dependent Choice is used only through the uniform finite-order bound for the sequence of distributions.

Facts & Assumptions

[F1]

Under Dependent Choice, a pointwise bounded family of distributions has a common finite-order estimate on each DK; a pointwise convergent sequence has a pointwise bounded range (Uniform finite order bounds for pointwise bounded distributions).

[F2]

Smooth multiplication acts by (av)(φ)=v(aφ) (Multiplication of a distribution by a smooth function).

Proof

Given: the sequences and limits in the statement, and Dependent Choice.

1.1

Fix a test φ with compact support K. By F1 and F3 there are C,m such that uj(ψ)Cpm(ψ) for every j and ψDK. Every (aja)φ is supported in K, and the finite product rule gives pm((aja)φ)2mqK,m(aja)pm(φ)0, where qK,m is the maximum of derivatives through degree m on K.

givenF1F2F3algebra
2.1

By F2 the difference of pairings is uj((aja)φ)+(uju)(aφ). The first term tends to zero by step 1.1 and the second by weak convergence applied to the fixed test aφ. Therefore (ajuj)(φ)(au)(φ). Since the test was arbitrary this is weak convergence. Zero multipliers and the zero test satisfy the same bounds, and on the empty domain all distributions are zero. The finite initial part of the sequence is covered by F1, with no unsupported arbitrary-net uniform bound.

step 1.1givenF1F2
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Extension by zero for distributions with ambient closed support

Statement

Let F be closed in Rn, let FΩ with Ω open, and let uD(Ω) have support contained in F. Then there is a unique distribution u~ on Rn restricting to u on Ω and with support contained in F. No compactness of F is required. The assertion holds in ZF and uses ambient closedness, not merely relative closedness in Ω.

Facts & Assumptions

[F1]

Compatible distributions on an arbitrary open cover glue uniquely (Distributions form a sheaf).

[F2]

A distribution vanishes on the complement of its support, and support is the complement of its largest vanishing open set (Support of a distribution).

Proof

Given: F,Ω,u as in the statement.

1.1

The sets Ω and RnF are open and cover Rn because FΩ. On their overlap ΩF, the distribution u vanishes by F2 since its support is contained in F. Hence u and the zero distribution on RnF are compatible.

givenF2
2.1

F1 glues them to u~. It restricts to u and is zero on RnF, so F2 gives support contained in F. Any other extension with this support must have the same two restrictions, and uniqueness in F1 makes it equal to u~.

step 1.1F1F2
3.1

If F is empty, F2 makes u=0 and the extension is zero; if Ω=Rn, the extension is u. Ambient closedness ensures that the second member of the cover is open. The proof therefore supplies no extension claim across a boundary when only relative closedness is known. There is no choice use beyond the choice-free sheaf theorem.

step 2.1F1F2
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Translation invariant test function operators are convolutions

Statement

A continuous complex-linear map L:D(Rn)C(Rn), with the compact-open topology in the target, commutes with all translations if and only if it has the form Lφ=uφ for a unique distribution u. Its values are smooth, and it is continuous into C(Rn) with uniform convergence of all derivatives on compact sets. Here Thf(x)=f(xh). These assertions hold in ZF.

Facts & Assumptions

[F1]

Convolution with a test is smooth, with derivatives on the test factor (Convolution with a test function is smooth).

[F2]

Stagewise continuity of a linear map from D into a locally convex space gives LF continuity (Test function lf topology universal property).

[F3]

Smooth parameter test families pair smoothly with distributions (Distribution pairing with smooth parameter families).

[F4]

Distributions satisfy a finite-order estimate on each fixed compact support (Local finite order characterization of distributions).

Proof

Given: a continuous linear L as in the statement.

1.1

Suppose L commutes with translations and put ψˇ(y)=ψ(y). Reflection maps DK to DK with unchanged derivative seminorms, so F2 makes it continuous on D. Evaluation at zero is continuous on C(Rn) for the compact-open topology. Consequently u(ψ)=(Lψˇ)(0) is a continuous linear test functional, hence a distribution.

givenF2
2.1

For fixed x, reflection of the test yφ(xy) is zφ(x+z)=Txφ(z). Thus u(φ(x))=(LTxφ)(0)=(TxLφ)(0)=Lφ(x). F1 (or F3 for this parameter family) shows this is smooth. If another distribution gives the same operator, evaluating its convolution with ψˇ at zero recovers its value on every ψ, so it equals u.

step 1.1givenF1F3
3.1

Conversely fix a distribution u, a source support K, a target compact H, and target derivative order r. All tests (αφ)(x) with xH, αr, and φDK are supported in the single compact HK. F4 there gives C,m, and F1 gives maxαrsupHα(uφ)Cpm+r(φ). This proves continuity from each stage into the smooth-function topology, hence continuity on D by F2. Direct substitution gives u(Thφ)(x)=u(φ(xh))=(Th(uφ))(x), proving translation commutation. Empty source or target compact sets give zero seminorms; the zero distribution gives the zero operator. No closed-graph theorem or choice is used.

step 2.1F1F2F4
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Complex l one functionals on finite measure spaces have bounded densities

Statement

Assume the Axiom of Choice. If (X,A,μ) is a finite measure space and Λ:L1(μ;C)C is bounded and complex-linear, then there is an essentially bounded complex measurable g such that Λ(f)=Xfgdμ(fL1(μ;C)). One may take g2Λ. The pairing contains no conjugation. AC is used through the finite-measure real Radon–Nikodym representation supplier.

Facts & Assumptions

[F1]

Under AC a bounded real functional on finite-measure Lp, 1p<, has a real integrable density representing it on every bounded measurable representative (On a finite-measure space, a bounded Lp functional is integration against its Radon-Nikodym density).

[F2]

Complex integration is defined through real and imaginary parts (Integrable real and complex functions, and their integrals).

[F3]

The assumed axiom is The Axiom of Choice.

[F4]

Dominated convergence yields L1 convergence under an integrable majorant (Dominated convergence).

[F5]

hh for integrable complex h (The modulus of an integral is bounded by the integral of the modulus).

Proof

Given: AC, the finite measure space and Λ; put M=Λ.

1.1

Restrict ReΛ and ImΛ to real L1 classes. They are real-linear with norm at most M. F1 with p=1, under F3, gives real h1,h2L1 representing them on bounded real measurable functions.

givenF1F3
2.1

Fix either density h and its real functional A. For k1, set Ek={h>M+1/k}. Its indicator is in L1 since the measure space is finite. Then (M+1/k)μ(Ek)Ekh=A(1Ek)Mμ(Ek), so μ(Ek)=0. Apply the same reasoning to {h>M+1/k} with the negative functional. Their countable union shows hM almost everywhere. Define g=h1+ih2; it is measurable and g2M almost everywhere. We may set it to zero on the explicitly determined null set where this bound fails.

step 1.1F2algebra
3.1

For a bounded complex function f=a+ib with real bounded a,b, complex linearity gives Λ(f)=Λ(a)+iΛ(b)=a(h1+ih2)+ib(h1+ih2)=fg, using F2. For arbitrary fL1, let fN=fmin(1,N/f), with zero value at f=0. Then fN is bounded, fNf pointwise and fNff, so F4 gives fNf10. Boundedness of Λ gives Λ(fN)Λ(f), while step 2.1 and F5 give (fNf)g2MfNf10. Passing to the limit proves the formula for all L1 classes.

step 2.1step 1.1F2F4F5
4.1

If M=0, the same level-set argument gives g=0 almost everywhere; if μ(X)=0, L1 is the zero space and take g=0. Indicator tests were used only on a finite-measure space, and truncations were explicitly defined. The full AC use is precisely F1, not a presumed complex duality theorem.

step 3.1F1F3
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Local structure of distributions as derivatives of continuous functions

Statement

Assume the Axiom of Choice. For uD(Ω) and compact KΩ, there are a continuous complex function f on Ω and a multi-index α with u(φ)=(αuf)(φ) for all φDK. More precisely, if the compact localization in the proof has order bound m, one can take α=(m+2,,m+2). In particular a global order bound m permits this same exponent for every compact localization. AC is used for the explicit Zorn extension and the bounded-density supplier (and covers the countable-choice integration interface).

Facts & Assumptions

[F1]

Distributions have compactwise finite-order estimates (Local finite order characterization of distributions).

[F2]

Compactly supported distributions act continuously on all smooth functions by cutoff extension (Compactly supported distributions extend to smooth functions).

[F3]

Smooth compact cutoffs exist (Test function cutoffs and euclidean localization), and smooth multiplication acts by test multiplication (Multiplication of a distribution by a smooth function).

[F4]

Under AC, a nonempty poset whose chains have upper bounds has a maximal element (Zorn's lemma).

[F5]

Absolutely integrable complex functions admit iterated integration in either order on sigma-finite products (Fubini's theorem for L^1 functions on a sigma-finite product).

[F6]

Under AC, bounded complex L1 functionals on finite measure spaces have essentially bounded densities for the bilinear integral (Complex l one functionals on finite measure spaces have bounded densities).

[F7]

Regular distributions and signed distribution derivatives use the bilinear conventions of Regular distribution from a locally integrable function and Distributional derivative.

[F8]

AC is assumed as in The Axiom of Choice; applying it to countable families also supplies Countable Choice. The complex Lebesgue FTC and integration by parts on finite intervals are available under that subcase (Complex integration by parts on intervals and decaying lines).

Proof

Given: AC, u, and compact KΩ.

1.1

Take χD(Ω) equal to one near K using F3. The distribution v=χu has compact support inside suppχ: it vanishes on the complement by its defining pairing. F2 therefore lets it act on restrictions of all smooth functions on Rn; denote that action by w(ψ)=u(χψΩ). F1 on suppχ and the finite product rule give w(ψ)Amaxγmsupsuppχγψ for some A,m. If u has global order at most m, its local estimate has that same exponent and multiplication does not raise it. Choose a nondegenerate open box B=i(ai,bi) containing suppχ compactly in its interior. It need not lie in Ω.

givenF1F2F3
2.1

Put k=(m+1,,m+1) and T=k on D(B). For γm let qi=kiγi1. Repeatedly integrating in each coordinate from ai gives [step 1.1, F5, F8] γψ(x)=a1x1anxni(xiti)qi1(qi1)! Tψ(t)dt. All lower endpoint derivatives vanish because ψ is compactly supported in B. The formula follows from the FTC in F8 by qi successive integrations; F5 changes the repeated integral over each simplex to the displayed polynomial kernel. Its kernel is bounded on the box by a finite constant depending on B,γ,m. Hence pm(ψ)CB,mTψL1(B). This also proves injectivity of T as a map to L1 classes: if Tψ=0 almost everywhere, all displayed integrals vanish, including the one for ψ. Thus on the complex subspace E=T(D(B))L1(B;C), (Tψ)=w(ψ) is well-defined and has bound (z)Mz1, M=ACB,m.

step 1.1F5F8
3.1

To extend this functional, regard L1(B;C) as a real normed space and set p(z)=Mz1. Consider all real-linear extensions of Re from the underlying real subspace E to real subspaces, dominated above by p, ordered by extension. This is a set of graphs; it is nonempty, since the original real part is dominated. The union of a nonempty chain is a well-defined real-linear functional on the union subspace, still dominated by p; the empty chain has the original functional as an upper bound. F4 and F8 give a maximal such extension h on a domain D.

step 2.1F4F8
4.1

If zD, define [step 3.1, algebra] Lz=supdD(h(d)p(dz)),Uz=infdD(p(d+z)h(d)). For d,eD, h(d)+h(e)=h(d+e)p(d+e)p(dz)+p(e+z), so every lower candidate is at most every upper candidate. The choices d=0 show p(z)LzUzp(z), so these are finite and c=Lz belongs to the interval. Define h(d+tz)=h(d)+tc. The decomposition is unique. For t>0 the upper bound on c applied to d/t gives domination by p(d+tz); for t<0 the lower bound applied to d/(t) gives the same, and t=0 is the old bound. Thus h contradicts maximality. Consequently D=L1(B;C).

step 3.1algebra
5.1

Set Λ(z)=h(z)ih(iz). Real linearity and Λ(iz)=iΛ(z) make it complex-linear. On E it equals since Re(iz)=Im(z). If Λ(z)0, put ω=Λ(z)/Λ(z). Then Λ(z)=ReΛ(ωz)=h(ωz)Mz1; the zero case has the same bound. F6 on the finite Borel Lebesgue measure of B gives a bounded Borel representative b, after setting its exceptional null values to zero, with w(ψ)=BbTψ.

step 4.1F6algebra
6.1

Extend b by zero outside B and define G(x)={tixi i}b(t)dt. It is continuous on all of Rn: for x,x, the symmetric difference of the two lower orthants intersected with B lies in the union of coordinate slabs of widths at most xixi. Thus G(x)G(x)bixixiji(bjaj). For a test ζ on Rn, F5 swaps t,x in the absolutely integrable function b(t)1tixi i1nζ(x): t lies in bounded B and x in the compact derivative support. F8 in each xi gives the inner integral (1)nζ(t), because all upper endpoint values vanish. Therefore G1nζ=(1)nbζ.

step 5.1F5F8
7.1

Apply step 6.1 to ζ=Tψ, ψD(B). Then w(ψ)=(1)nGk+1ψ. Put f=(1)kGΩ and α=k+1. Since α=k+n, F7 gives αuf(φ)=(1)nGαφ=w(φ)=u(φ) for every φDK. The function f is continuous and locally integrable. Empty K has only the zero test, so f=0,α=0 suffices there; zero bounds in the construction give zero functionals and cause no division by a norm. This proves the claim and the stated exponent control.

step 6.1step 5.1step 1.1F7
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Compact support continuous primitive representation

Statement

Assume AC. Let uD(Ω) have compact support K and global order at most m. For every open V with KVΩ, there are finitely many continuous functions gβ on Ω, each compactly supported in V, such that u=0βim+2βugβ. Zero functions may fill unused indices. Only the AC use in the local representation theorem is needed beyond the stated elementary operations.

Facts & Assumptions

[F1]

Under AC, local representation by a continuous function can use the multi-index α=(m+2,,m+2) when the localized distribution has order bound m (Local structure of distributions as derivatives of continuous functions).

[F2]

Distributional Leibniz's rule holds for smooth multipliers (Leibniz rule for distributions).

[F3]

A compact set inside an open set admits a smooth compact cutoff equal to one near it (Test function cutoffs and euclidean localization).

[F4]

A compactly supported distribution pairs with smooth functions by cutoff, and vanishes on smooth functions zero near its support (Compactly supported distributions extend to smooth functions).

[F5]

The assumed axiom is The Axiom of Choice.

Proof

Given: u,K,m,V and AC.

1.1

Use F3 in V to choose θD(V) equal to one near K, and put L=suppθ. By F1 and F5 there is continuous f on Ω with u=αuf on DL, where each αi=m+2. For every test φ, F4 gives u(φ)=u(θφ), and θφDL, hence u=θαuf globally on Ω.

givenF1F3F4F5
2.1

For any distribution w, F2 implies the inverse product identity [step 1.1, F2, algebra] θαw=βα(1)αβ(αβ)β((αβθ)w). Indeed expand each derivative on the right by F2. The coefficient of (αγθ)γw is (αγ)δαγ(1)αγδ(αγδ). The sum is the product of the binomial expansions of (11)αiγi, so it is zero unless γ=α, when it is one. Thus precisely the left side remains.

step 1.1F2algebra
3.1

Apply step 2.1 to w=uf and define gβ=(1)αβ(αβ)(αβθ)f. These functions are continuous and supported in LV. Multiplying a regular functional by the smooth factor yields its pointwise product by the defining integral, so step 1.1 and the identity give the required sum. There are only i(m+3) indices. If K is empty, u=0 by support locality and all functions may be zero. For m=0 the bound is still βi2. No further infinite selection occurs.

step 2.1step 1.1F4
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Global locally finite structure of distributions

Statement

Assume AC. Every uD(Ω) has a representation u=ααugα with continuous complex functions gα on Ω, such that every compact subset of Ω meets the supports of only finitely many gα. Thus each test evaluation of the sum is finite. If u has global order at most m, the functions can be taken zero unless αim+2 for every coordinate, so only finitely many multi-indices are needed. AC is used in the local representation supplier and to select its representations for the countably many localized pieces.

Facts & Assumptions

[F1]

A compactly supported distribution of order at most m has a finite continuous-function derivative representation with coefficient supports in any prescribed open neighborhood, and coordinate exponents at most m+2, under AC (Compact support continuous primitive representation).

[F2]

Restrictions of distributions compose, and compatible distributions on an arbitrary open cover glue uniquely (Distributions form a sheaf).

[F3]

There is an at most countable locally finite smooth partition of unity with compact supports on Ω (Test function cutoffs and euclidean localization).

[F4]

AC is assumed as in The Axiom of Choice.

[F5]

Compactly supported distributions have global finite order (Compactly supported distributions have global finite order).

[F6]

A smooth multiplier acts by test multiplication (Multiplication of a distribution by a smooth function).

Proof

Given: AC and uD(Ω).

1.1

Take a partition (ηi) from F3, indexed by positive integers or a finite initial segment, and discard zero functions. Write Si=suppηi. These nonempty compacts admit a locally finite family of relatively compact open neighborhoods Vi: set εi=min(1/i,1,dist(Si,RnΩ)/2), interpreting distance to the empty set as infinity, and take Vi={x:dist(x,Si)<εi}. Their closures are compact inside Ω. To see local finiteness, fix a ball B(x,2r) compactly inside Ω. Its closure meets only finitely many Si by the given local finiteness and compactness. The remaining supports lie outside this ball; for all sufficiently large i, εi<r, so their Vi miss B(x,r). Only finitely many exceptions remain.

givenF3
2.1

Define ui=ηiu. By F6 its support is contained in Si, so F5 gives it some global order mi. F1 gives a finite representation ui=ααugi,α with continuous coefficient functions compactly supported in Vi. Use F4 to select one such finite representation for every index, including an order and its coefficient tuple; the sets of possible tuples are nonempty by F1 and F5. Set gi,α=0 outside its finite index set.

step 1.1F1F4F5F6
3.1

Put gα=igi,α. Step 1.1 makes these sums locally finite, hence continuous. A locally finite union of closed coefficient supports is closed: near any point only finitely many supports occur, and the complement of that finite union is open. Consequently suppgα is contained in the union of the corresponding coefficient supports. A compact H meets only finitely many Vi, and each of those indices has only finitely many coefficients, so H meets only finitely many suppgα.

step 2.1step 1.1
4.1

Fix α and cover Ω by open balls whose compact closures lie in Ω. Each closure meets only finitely many Vi, so on each ball the regular functional of gα is the finite sum of the corresponding regular distributions ugi,α. These local distributions agree on overlaps because both finite expressions integrate the same locally finite pointwise sum. F2 therefore glues them to a distribution on Ω, and uniqueness identifies that distribution with the regular functional denoted ugα. For a test φ, only finitely many partition supports meet its compact support, so the partition identity from F3 and linearity give u(φ)=iu(ηiφ)=iui(φ). Insert the representations from step 2.1. The same compact meets only finitely many Vi, and all distribution derivatives of regular coefficients supported elsewhere vanish on the test. Thus both sums may be interchanged as finite sums, and finite linearity of the regular integral gives u(φ)=α(αugα)(φ).

step 3.1step 2.1F2F3F6
5.1

If u has global order at most m, every ui=ηiu has order at most m: on any fixed compact, the product rule bounds pm(ηiφ) by a finite constant times pm(φ), without increasing the order. Choose all representations in step 2.1 using this same m. Then F1 permits only the fixed finite index box αim+2, proving the final assertion. Empty Ω or u=0 permits all coefficients zero. The partition and open enlargements use no choice; the representation selection and its supplier have the explicit AC use.

step 4.1step 2.1F1F4F6
LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Finite sums of product tests are dense on product open sets

Statement

For integers p,q1 and open URp, VRq, every ΦD(U×V) is a limit in the LF test topology of finite sums of products f(x)g(y) with fD(U) and gD(V). All approximating supports and the target support lie in one compact product inside U×V. This holds in ZF.

Facts & Assumptions

[F1]

Nonnegative smooth compact cutoffs equal to one near compact sets exist (Test function cutoffs and euclidean localization).

[F2]

The smooth-bump rescaling formula is ρε(z)=εdρ(z/ε) (The mollifier family generated by a unit-mass smooth bump). In this proof all auxiliary integrals and mass normalizations are Riemann integrals; no Lebesgue approximate-identity theorem is invoked.

[F3]

Uniform limits of functions and their first derivatives on coordinate intervals identify the derivative of the limit (If continuously differentiable functions converge at one point and their derivatives converge uniformly on a closed interval, then the functions converge uniformly to a differentiable function whose derivative is the derivative limit), componentwise for complex functions.

[F4]

Compactly supported Riemann integrands obey diffeomorphic change of variables (A compactly supported Riemann integrand admits the global change-of-variables formula from a diffeomorphism near the relevant compact preimage), used only for translations and positive scalar dilations.

[F5]

Finite-dimensional Riemann integrals obey linearity and the absolute bound (Linearity, monotonicity, the absolute-value estimate and coordinate-slice additivity for the Riemann integral in Rm); all tagged grid sums of an integrable function converge with mesh to its integral (The multidimensional Darboux and tagged-mesh definitions of the Riemann integral agree); and continuous product integrands obey Riemann Fubini (Riemann--Fubini on product rectangles, with lower and upper section integrals and content-zero exceptional sections). Complex statements follow by real and imaginary parts.

[F6]

Common compact support and uniform convergence of every derivative give LF test convergence (Sequential convergence in test function space).

Proof

Given: integers p,q1, open URp and VRq, and ΦD(U×V), extended smoothly by zero to the whole Euclidean product.

1.1

If Φ=0 use the constant sequence of empty sums. Otherwise let A,B be the compact coordinate projections of its support. Take nonnegative smooth compact bumps in the two coordinate spaces from F1 and divide each by its positive finite Riemann integral. Denote them by ρ,σ; positivity follows since each is one on a ball, and finiteness from bounded compact support. Fix radii Rρ,Rσ containing their supports. Choose ε0>0 such that A+B(0,ε0Rρ)U and B+B(0,ε0Rσ)V, possible by compact interior margins. These two compact neighborhoods form the fixed support product L.

givenF1F2F5
2.1

For 0<εε0 form the Riemann integral [step 1.1, F2, F3, F4, F5] Iε(x,y)=Φ(a,b)ρε(xa)σε(yb)dadb. A fixed box containing suppΦ bounds the parameter integral. On any fixed compact target box, take uniform parameter grids with midpoint tags. For every mixed target derivative Dγ, differentiating the corresponding finite sums gives the tagged sums for the Dγ-derivative of the integrand. Joint uniform continuity on the compact parameter-target product makes these sums converge uniformly in (x,y): their error from the derivative-integral candidate is at most the parameter-box volume times the largest oscillation on a grid cell. The tagged-sum theorem in F5 identifies the pointwise candidate with the Riemann integral, and repeated applications of F3 on coordinate intervals identify it with DγIε. Thus Iε is smooth and supported in L.

Applying F4 in the two coordinate blocks and then F5 gives Iε(x,y)=Φ(xεs,yεt)ρ(s)σ(t)dsdt. The same uniform tagged-sum argument, now on the fixed support box of ρσ, gives for each mixed derivative DγIε(x,y)=DγΦ(xεs,yεt)ρ(s)σ(t)dsdt. [step 1.1, F2, F3, F4, F5]

3.1

The product kernel has Riemann integral one by F5, is nonnegative and has fixed bounded support. Uniform continuity of the globally smooth compactly supported DγΦ therefore bounds supDγIεDγΦ by its modulus of continuity at εRρ2+Rσ2, tending to zero. Uniform continuity on all space follows from uniform continuity on a compact neighborhood of its support and vanishing outside it. This proves convergence of every derivative as ε0.

step 2.1F5
4.1

Put P0=0. For j1, set εj=ε0/(j+1) and, for the original (a,b)-integral in step 2.1, take uniform product grids with midpoint tags in the fixed parameter box. Each tagged sum has the separated form CCΦ(aC,bC)ρεj(xaC)σεj(ybC). Terms with zero coefficient are omitted. Every remaining tag is in the nonzero set of Φ, so its factor supports lie in the two fixed compact neighborhoods defining L, even if the parameter box itself is not contained in U×V. For each j1, choose the least grid level for which the error from Iεj in all target derivatives of total order at most j is less than 1/j. Such a level exists by the uniform derivative convergence of those tagged sums established in step 2.1. The least-level rule is a defined integer, with no countable choice. Call the resulting finite product sum Pj.

step 3.1step 2.1F2F5
5.1

For fixed γ, the difference Dγ(PjΦ) is bounded, for jmax(1,γ), by 1/j+supDγ(IεjΦ), which tends to zero by step 3.1. All supports are in L, so F6 gives PjΦ in D(U×V). If either domain is empty only the zero test occurs, covered in step 1.1. All integrations were of continuous compactly supported Riemann integrands and all grids were specified, so no choice axiom entered.

step 4.1step 3.1step 1.1F6
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Tensor product distributions and iterated pairings

Statement

For uD(U) and vD(V) the tensor candidate is a distribution on U×V, uniquely determined by (uv)(φψ)=u(φ)v(ψ). Its two iterated pairing orders agree, and supp(uv)=suppu×suppv. Three-factor pairings associate. Moreover xα(uv)=(αu)v and yβ(uv)=u(βv). All claims hold in ZF.

Facts & Assumptions

[F1]

The tensor candidate is well-defined and linear, and has the stated values on product tests (Tensor product of distributions).

[F2]

Compactwise finite-order estimates characterize distributions (Local finite order characterization of distributions).

[F3]

Parameter differentiation passes through a distribution pairing with locally common compact supports (Distribution pairing with smooth parameter families).

[F4]

Finite product-test sums are dense in the product LF test space with a common compact support (Finite sums of product tests are dense on product open sets).

[F5]

Support is the complement of the largest vanishing open set (Support of a distribution), and vanishing on an open cover implies vanishing on its union (Distributions form a sheaf).

[F6]

Distribution derivatives are signed test transposes (Distributional derivative).

Proof

Given: the distributions on the open factor domains.

1.1

Fix compact KU×V and let KU,KV be its compact projections. F2 gives bounds Cu,pmu for u on KU and Cv,pmv for v on KV. For ΦDK, the inner paired function has support in KU by F1, and F3 gives its derivatives by pairing x derivatives of Φ. Applying the two bounds yields (uv)(Φ)CuCvmaxαmu,βmvsupKU×KVxαyβΦCuCvpmu+mv(Φ). F2 proves it is a distribution. The same argument applies with the pairing order reversed.

givenF1F2F3
2.1

The two orders have the same values on all products by F1. Their difference is a continuous functional zero on all finite product sums, so F4 makes it zero on every test. The same reasoning shows uniqueness of any distribution with the product values.

step 1.1F1F4
3.1

If xsuppu, choose an open neighborhood A of x on which u=0. On tests supported in A×V, the outer test in F1 is supported compactly in A, so the tensor vanishes. If ysuppv, the reversed pairing of step 2.1 gives the corresponding vanishing near U×{y}. F5 proves support containment in the product. Conversely, if (x,y) lies in that product, every open neighborhood contains A×B with xA,yB. F5 implies there exist tests φD(A), ψD(B) with nonzero pairings; otherwise one factor would vanish on that neighborhood. F1 makes their product pairing nonzero. Thus the tensor does not vanish on any neighborhood of (x,y), proving equality of supports. Only two local witnesses were used.

step 2.1F1F5
4.1

Applying the distribution construction to two blocks at a time gives distributions (uv)w and u(vw) on a triple product, each taking value u(φ)v(ψ)w(η) on pure triple tests. To prove equality, fix η: their difference on H(x,y)η(z) is a continuous functional in H, since multiplying by this fixed test preserves compact support and bounds derivatives by fixed constants. F4 in U×V makes it zero for every H. F4 again, now for (U×V)×W, makes the original difference zero on every triple test. This proves associativity and agreement of the nested orders.

step 3.1step 2.1F1F4
5.1

For the x derivative, F6 applied to the outer distribution and F3 applied to the inner test give ((αu)v)(Φ)=(1)α(uv)(xαΦ), the asserted derivative. For the y derivative apply F6 directly to the inner pairing. If a factor is zero, all formulas give zero and the support product is empty; empty domains behave the same way. Order-zero derivatives are identities. All estimates and density passages were choice-free.

step 4.1F1F3F6
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Convolution of distributions when one has compact support

Definition

Let u,vD(Rn), n1, with at least one compact support. Write S=suppu, T=suppv and a(x,y)=x+y. For ψD(Rn) put Eψ=(S×T)a1(suppψ). For any χD(R2n) equal to one on a neighborhood of Eψ, set the candidate value Cu,v,ψ(χ)=(uv)(χψa).

The tensor and its support are supplied by Tensor product distributions and iterated pairings and Support of a distribution. The set Eψ is compact: if S is compact, it is a closed subset of the compact set S×(suppψS); interchange factors if T is compact. A cutoff therefore exists by Test function cutoffs and euclidean localization. The lemma Convolution of distributions is well defined under the support hypothesis proves that the candidate value is independent of χ and is a continuous linear functional of ψ; define (uv)(ψ) to be this common value. If both supports are compact it agrees with the smooth extension pairing of Compactly supported distributions extend to smooth functions. Empty support gives the zero candidate, and χ=0 is allowed when Eψ is empty. No infinite selection is part of the definition.

LemmaStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Convolution of distributions is well defined under the support hypothesis

Statement

For an integer n1 and distributions u,v on Rn, if at least one has compact support, the convolution candidate is cutoff-independent and defines a distribution. It is bilinear, commutative, and supp(uv)suppu+suppv. These claims hold in ZF.

Facts & Assumptions

[F1]

The candidate pairs uv with χ(x,y)ψ(x+y); the relevant support intersection is compact (Convolution of distributions when one has compact support).

[F2]

Tensor products are distributions with product support and interchangeable pairing orders (Tensor product distributions and iterated pairings).

[F3]

Compact sets admit smooth compact cutoffs equal to one on neighborhoods (Test function cutoffs and euclidean localization).

[F4]

A distribution vanishes on tests supported outside its support, by the support definition and locality (Support of a distribution, Distributions form a sheaf).

[F5]

Compactwise finite-order bounds characterize distributions (Local finite order characterization of distributions).

Proof

Given: an integer n1, u,v on Rn, and supports S,T, with one compact.

1.1

Two allowed cutoffs have difference zero near Eψ of F1. At every point of S×T outside Eψ, the function ψ(x+y) vanishes on a neighborhood. Thus the compact test (χχ~)ψ(x+y) has support disjoint from S×T. F2 and F4 make its pairing zero. This proves independence; if Eψ is empty the zero cutoff gives zero.

givenF1F2F4
2.1

Fix compact KRn. F1 and F3 give one cutoff for EK=(S×T)a1(K), valid for every test supported in K. The compact support L of this cutoff is fixed. F5 gives an order m tensor estimate there. The ordinary product and chain rules give pm(χψa)Aχ,mpm(ψ): each mixed derivative of ψ(x+y) is a derivative of ψ of the same total order, and the finite Leibniz sum has bounded cutoff coefficients. The candidate is linear in ψ by using this same cutoff for a finite sum, and F5 proves continuity. Bilinearity in the distributions follows similarly from one cutoff for the finite union of their relevant support intersections whenever each convolution is defined under the compact-factor condition.

step 1.1F1F2F3F5
3.1

Reflection of the two coordinate blocks sends an allowed cutoff to an allowed cutoff for vu. By F2 the tensor values agree after this interchange. One may verify the coordinate interchange first on product tests and then on their dense span, as in F2. Hence uv=vu.

step 2.1F1F2
4.1

Suppose S is compact and both sets are nonempty. If zS+T, the continuous function sdist(zs,T) is positive on S, hence has positive minimum d. Every z with zz<d/2 remains outside S+T. Thus S+T is closed. The case of compact T follows by interchange, and empty summands give the empty closed set. A test supported in its complement has Eψ=, so step 1.1 gives zero. F4 proves the support inclusion. Zero factors give the zero distribution; no lower bound or equality of convolution supports is asserted.

step 3.1step 1.1givenF4
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Mollifier approximation in distributions

Statement

Assume Countable Choice for Lebesgue integration. Let n1, let ΩRn be open, and let uD(Ω). Fix ρD(Rn) with ρ=1, and put ρε(x)=εnρ(x/ε) for ε>0. The smooth local convolution fε(x)=u(ρε(x)) is defined on Vε={x:xεsuppρΩ}. Its regular distribution converges weakly to u locally: every test ψD(Ω) is supported in Vε for all sufficiently small positive ε, and fεψu(ψ).

Facts & Assumptions

[F1]

The stated scaling defines a unit-mass-bump mollifier family (The mollifier family generated by a unit-mass smooth bump).

[F2]

Local convolution on the safe domain is smooth, and all derivatives commute with the distribution pairing (Convolution with a test function is smooth).

[F3]

For compactly supported smooth parameter integrands, integration commutes with distribution pairing under Countable Choice (Distribution pairing with smooth parameter families).

[F4]

Compactly supported Riemann substitution is valid, and real and imaginary parts of bounded smooth box integrands have equal Riemann and Lebesgue integrals under Countable Choice (A compactly supported Riemann integrand admits the global change-of-variables formula from a diffeomorphism near the relevant compact preimage, Riemann–Lebesgue comparison for distribution test integrands).

[F5]

A distribution has a finite-order estimate on every fixed compact test support (Local finite order characterization of distributions).

[F6]

Locally integrable functions have regular functionals (Regular distribution from a locally integrable function), and under Countable Choice these embed into distributions (Locally integrable functions embed in distributions). Countable Choice is assumed exactly for the Lebesgue-integral interfaces (The Axiom of Countable Choice (ACω)).

Proof

Given: an integer n1, u,Ω,ρ, and Countable Choice.

1.1

Choose R>0 with suppρB(0,R). For a nonempty compact test support KΩ choose d>0 such that K+B(0,2d)Ω. If εR<d, then KVε, even with a fixed compact neighborhood inside it. F2 gives smoothness there. Hence fε is Borel and bounded on every compact subset of its safe domain, so it is locally integrable; F6 types its integral functional as a regular distribution. No nonnegativity or symmetry of ρ is required.

givenF1F2F6
2.1

Apply F3 to F(x,y)=ψ(x)ρε(xy), with parameter x in Vε, integrating on a compact neighborhood of K contained in that domain. Its slices have a common compact support in Ω there. Outside K the integrand is zero. Thus F3 and F6 give [step 1.1, F3, F6] fε(x)ψ(x)dx=u(ψε),ψε(y)=ρε(xy)ψ(x)dx. By the affine substitution x=y+εz, justified for these compact smooth integrands by F4, ψε(y)=ρ(z)ψ(y+εz)dz. All these tests are supported in K+B(0,d).

step 1.1F3F4F6
3.1

Extend ψ smoothly by zero to Rn. Its every derivative is uniformly continuous. Differentiating the last compact integral (by uniform difference-quotient estimates, or F3's derivative clause) yields [step 2.1, F3, F4] pm(ψεψ)ρL1maxαmsupy,hεRαψ(y+h)αψ(y)0. Here unit mass subtracts ψ(y) inside the integral; the displayed estimate is also the Riemann integral triangle estimate for continuous compact functions. F5 on the common compact support now gives u(ψε)u(ψ). This proves the assertion with step 2.1. If the test or domain is empty, both sides are zero; ε=0 is a limit endpoint, not a defined kernel. Countable Choice enters only through F3, F4 and the Lebesgue regular-distribution interpretation.

step 2.1F3F4F5F6
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

A distribution with zero derivatives on a connected open set is constant

Statement

Assume Countable Choice for the Lebesgue regular-distribution convention. If ΩRn is nonempty, open and connected and ju=0 for every 1jn, then there is a unique cC such that u(ψ)=cψ for every test. Conversely every constant regular distribution has zero first derivatives. On a disconnected open set constants may differ on different connected components.

Facts & Assumptions

[F1]

Smooth local convolutions satisfy j(uρε)=(ju)ρε (Convolution with a test function is smooth).

[F2]

Unit-mass mollifications converge locally in distribution pairings under Countable Choice (Mollifier approximation in distributions).

[F3]

A compact ball admits a nonnegative smooth compact cutoff, so a nonzero such bump inside any open ball can be normalized by its positive finite integral (Test function cutoffs and euclidean localization).

[F5]

Distributions equal on an open cover are equal globally (Distributions form a sheaf).

[F6]

Classical derivatives of smooth regular distributions agree with distribution derivatives under Countable Choice (Distributional differentiation is continuous and commutes). We assume The Axiom of Countable Choice (ACω) for F2 and this regular interpretation.

Proof

Given: the vanishing first derivatives and the stated choice assumption.

1.1

Take any open ball B with compact closure in Ω. For all sufficiently small ε>0, F1 gives a smooth mollification on a neighborhood of B with every first derivative zero. Along the segment between any two points of B, the chain rule gives derivative zero; F4 with bound zero makes their values equal. Denote this value by cε.

givenF1F2F4
2.1

Choose ηD(B) with η=1 using F3. Then F2 gives cε=(uρε)ηu(η)=cB. For every ψD(B), F2 therefore gives u(ψ)=limcεψ=cBψ. A unit-integral test also shows uniqueness of this constant on B.

step 1.1F2F3
3.1

If two such balls overlap, their intersection contains an open ball and hence a unit-integral test by F3. Evaluation on that test proves that their constants agree. Consequently there is a well-defined locally constant function c(x) on Ω, whose value is the unique constant of any sufficiently small ball about x. For a fixed x0, the set {x:c(x)=c(x0)} and its complement are open. It is nonempty, so connectedness forces the complement empty. F5 applied to the ball cover yields u=c(x0) as a regular distribution. A unit-integral test anywhere proves uniqueness.

step 2.1givenF3F5
4.1

F6 proves the converse, since the classical first derivatives of a constant vanish. In any open subset of Euclidean space each connected component is open: a ball about a point is connected and belongs to its component. Applying the preceding argument in each component gives independent constants. Conversely such a componentwise constant function is locally constant, hence smooth, and F6 gives zero derivatives. On the empty domain the sole distribution is zero but its representing constant is not unique; this explains the nonempty hypothesis. Countable Choice is inherited only from the integral mollification and regular-derivative clauses, not from the overlap argument.

step 3.1F6
CorollaryStatement: AI-adaptedProof: AI-adaptedaudited 2026-09-13Open item page →

Smooth functions are weakly dense in distributions

Statement

Assume Countable Choice for Lebesgue integration. For every integer n1, every open ΩRn, and every uD(Ω), there is a sequence fjD(Ω) whose regular distributions converge weakly to u. In particular smooth regular distributions are weakly dense in D(Ω).

Facts & Assumptions

[F2]

Smooth multiplication defines χju (Multiplication of a distribution by a smooth function); a distribution whose support is ambient closed has a unique zero extension (Extension by zero for distributions with ambient closed support).

[F3]

Local convolution is smooth (Convolution with a test function is smooth). Compact smooth parameter integrals commute with distribution pairing (Distribution pairing with smooth parameter families); compact Riemann integrals admit affine substitution (A compactly supported Riemann integrand admits the global change-of-variables formula from a diffeomorphism near the relevant compact preimage) and agree with their complex Lebesgue integrals under Countable Choice (Riemann–Lebesgue comparison for distribution test integrands). Distributions obey finite-order estimates on a fixed compact test support (Local finite order characterization of distributions). The weak-convergence conclusion of Mollifier approximation in distributions is consistent with, but does not itself assert, the reflected-test estimates below.

[F4]

A distribution vanishes on tests supported away from its support (Support of a distribution).

[F5]

Under Countable Choice, locally integrable functions define regular distributions (Locally integrable functions embed in distributions). Countable Choice also supplies the Lebesgue interfaces in F3 and a sequence of cutoffs in F1 (The Axiom of Countable Choice (ACω)).

Proof

Given: u and Countable Choice.

1.1

If Ω= use fj=0 for every jN. Otherwise set K0= and, for j1, set Kj={xRn:xj, dist(x,RnΩ)1/j}, with distance to the empty set interpreted as infinity. The distance function is one-Lipschitz, so F1 makes each Kj compact as a closed bounded set; the strict inequalities j<j+1 and 1/j>1/(j+1) give KjintKj+1. If LΩ is compact, finitely many balls B(xi,ri/2) cover L with B(xi,ri)Ω; hence L is bounded and has distance at least mini(ri/2)>0 from the complement, so LKj for all sufficiently large j. Put χ0=0 and use Countable Choice with F1 to choose, for every j1, a cutoff χj equal to one near Kj. The product χju vanishes outside suppχj directly from its definition, so its support is compact and ambient closed. Let wj be its zero extension by F2. Apply F1 with K={0} and Ω=Rn to obtain a nonnegative bump η equal to one near zero. Its Lebesgue integral c is finite and positive, so ρ=η/c is a unit-mass bump; compactness of its support gives R>0 with suppρB(0,R). For each j1 with nonempty cutoff support, take dj to be the smaller of one and half the distance from suppχj to RnΩ; for empty support put dj=1. Then dj>0 and suppχj+B(0,dj)Ω. Set εj=min(1/j,dj/(2R)).

givenF1F2F5
2.1

Put f0=0, and for j1 define fj=wjρεj. F3 makes the latter smooth on all Rn. If x is outside the closed sum suppχj+εjsuppρ, the test yρεj(xy) has support disjoint from suppwj. F4 gives fj(x)=0. The sum is compact and lies in Ω by step 1.1. Thus every fjΩ belongs to D(Ω), and F5 makes its integral functional a distribution.

step 1.1F3F4F5
3.1

For fixed ψD(Ω) extend it by zero to Rn. For j1, apply F3's parameter-pairing lemma to ψ(x)ρεj(xy) and then affine substitution x=y+εjz. This gives fjψ=wj(ψεj)=u(χjψεj), with ψε(y)=ρ(z)ψ(y+εz)dz. All sufficiently small ε have these test supports in one compact LΩ. By step 1.1, LKj for all sufficiently large j. Then χjψεj=ψεj. For each multi-index α, differentiating the compact integral and using ρ=1 gives supyαψε(y)αψ(y)ρL1supy,hεRαψ(y+h)αψ(y)0. Uniform continuity of every derivative of the compactly supported smooth ψ proves the limit. F3's finite-order estimate on L now gives u(ψεj)u(ψ), proving weak convergence. A convergent sequence meets every neighborhood of its limit, which proves density. Zero u gives the zero sequence, and Countable Choice has precisely the uses in F5.

step 2.1step 1.1F1F2F3F5
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Associativity of distribution convolution under compact support

Statement

If u,v,wD(Rn) and at least two have compact support, both bracketings exist and (uv)w=u(vw). Whenever at least one of u,v has compact support, α(uv)=(αu)v=u(αv). For every u, δ0u=u and (αδ0)u=αu. These assertions hold in ZF.

Facts & Assumptions

[F1]

Permitted convolution is bilinear, commutative, cutoff-independent and supported in the sum of the supports (Convolution of distributions is well defined under the support hypothesis).

[F2]

Tensor products have product support, associate, and commute with factor derivatives (Tensor product distributions and iterated pairings).

[F3]

Derivatives are signed test transposes; Dirac evaluates a test at zero (Distributional derivative, Dirac delta and its derivatives).

[F4]

Compactly supported distributions extend to smooth functions by a cutoff equal to one near their support (Compactly supported distributions extend to smooth functions); such cutoffs exist (Test function cutoffs and euclidean localization).

[F5]

Support means local vanishing, and multiplication by a smooth function is test multiplication (Support of a distribution, Multiplication of a distribution by a smooth function).

Proof

Given: the indicated compact-factor hypotheses.

1.1

First suppose all three supports are compact. By F4 every pairing with a smooth function may use a product of cutoffs equal to one near the three supports. Expanding either bracketed convolution on a test ψ then gives the same nested pairing ux(vy(wz(ψ(x+y+z)))): for example the inner w pairing is smooth, and the smooth extension of uv pairs it with x+y; inserting the three fixed cutoffs makes this exactly the tensor definition. F2's associativity identifies the bracketings. The equality holds also for all smooth ψ by the same cutoffs.

givenF1F2F4
2.1

In the general case let q be the possibly noncompact factor and S,T the two compact supports of the other factors. For a fixed compact test support K, the closed set E=suppq(KST) is compact. Choose θ=1 near E with compact support, and write q=q0+q1, where q0=θq. The support of q1 is contained in suppq and avoids a neighborhood of E, so K is disjoint from S+T+suppq1. This last sum is closed: add the compact set S+T to the closed support of q1, using F1's compact-sum argument. Every permitted bracketing containing q1 has support in that sum by F1 twice, and hence pairs to zero on tests supported in K. Both bracketings exist: either an inner pair is compactly supported, or the outer factor is one of the compact factors. Bilinearity therefore reduces both bracketings on this test to those with q0. They agree by step 1.1. Since K was arbitrary, associativity follows.

step 1.1givenF1F4F5
3.1

Derivatives have support contained in the original support by F3 and F5, so all asserted convolutions are permitted. For a test ψ use a cutoff χ equal to one near (suppu×suppv)a1(suppψ), where a(x,y)=x+y. The tensor derivative formula and F3 yield [step 2.1, F1, F2, F3, F5] ((αu)v)(ψ)=(1)α(uv)(xα(χψa)). Every term differentiating χ is zero near the tensor support: on the relevant intersection all such derivatives vanish, and outside it all derivatives of ψa vanish locally. These terms therefore pair to zero. The remaining term is (1)α(uv)(αψ), giving the first identity. Differentiating in y gives the other one, with the same total sign.

step 2.1F1F2F3F5
4.1

In the cutoff definition of δ0u, take a product cutoff with its first factor equal to one near zero and its second equal to one near suppψ. Evaluating the first pairing at zero leaves exactly u(ψ) by F3. The differentiated identity follows from step 3.1. Zero factors give zero throughout; order zero gives the identity derivative. Only finitely many cutoffs occur for each fixed test. No associativity is asserted merely from pairwise existence without the stated two-compact-factor hypothesis.

step 3.1F1F3F4
TheoremStatement: AI-adaptedProof: AI-adaptedjudge pass (gpt-5.6-terra)audited 2026-09-13Open item page →

Closed bounded test function sets are compact

Statement

Assume Countable Choice and Dependent Choice. Every closed bounded subset B of D(Ω) is compact in its LF topology.

Facts & Assumptions

[F1]

Bounded tests have a common compact support and uniform bounds on every derivative seminorm (Bounded test function sets have common compact support).

[F2]

The fixed-support space DK is complete for d(f,g)=m02m1min(1,pm(fg)) (Fixed support test function spaces are complete).

[F3]

Under the stated choice assumptions, equicontinuous pointwise-bounded real families on a nonempty compact metric space have compact sup-norm closure (Arzelà--Ascoli for real C(K) under Countable Choice and Dependent Choice: compact closure iff equicontinuous and pointwise bounded).

[F5]

The stage inclusion DKD(Ω) is continuous (Test function lf topology universal property).

Proof

Given: a closed bounded B and F7.

1.1

Empty B is compact. Otherwise F1 gives compact KΩ containing all supports, with Mm=supfBpm(f)<. Regard the tests as globally smooth zero extensions and choose a nondegenerate closed box Q whose interior contains K. For every multi-index α, the real and imaginary parts of αfQ, fB, are uniformly bounded by Mα. Their segment derivatives have norm at most nMα+1 times the segment direction norm. F6 therefore gives a common Lipschitz bound on Q, proving equicontinuity. F3 applies to each real family.

givenF1F3F6F7
2.1

Fix ε>0 and choose N with m>N2m1<ε/2. For each of the finitely many real and imaginary derivative families through order N, F3 and F4 give a finite sup-norm δ-net, with 0<δ<ε/8. Assign each fB the first net center within δ for each coordinate, using fixed finite listings. There are finitely many joint labels. Choose one member of each nonempty label class. If f,g have the same label, their real and imaginary derivative differences through order N are each less than 2δ, so pN(fg)<4δ<ε/2. F2's metric then gives d(f,g)<ε. The finitely many representatives form an ε-net for B, proving total boundedness without an infinite diagonal selection.

step 1.1F2F3F4
3.1

By F5 the inverse image of the LF-closed set B in DK is closed; it is just B. A Cauchy sequence in B converges in DK by F2 and its limit belongs to B by closedness. Thus B is complete. F4 and step 2.1 imply metric compactness in DK. For any LF-open cover of B, inverse images under F5 give a stage-open cover; a finite subcover there is a finite subcover in the LF space. This proves the required compactness. If K has empty interior, all its tests vanish and B is a subset of the singleton zero space. F7 is used through F3 and F4, with no stronger choice.

step 2.1F2F4F5F7

5 · Examples, counterexamples and false statements

None yet.

Sources