The following notation will be used throughout this article:
In this section, we will formally define real-valued distributions on U. With minor modifications, one can also define complex-valued distributions, and one can replace R n {\displaystyle \mathbb {R} ^{n}} with any (paracompact) smooth manifold.
Note that for all j , k ∈ { 0 , 1 , 2 , … , ∞ } {\displaystyle j,k\in \{0,1,2,\ldots ,\infty \}} and any compact subsets K and L of U, we have: C k ( K ) ⊆ C c k ( U ) ⊆ C k ( U ) C k ( K ) ⊆ C k ( L ) if K ⊆ L C k ( K ) ⊆ C j ( K ) if j ≤ k C c k ( U ) ⊆ C c j ( U ) if j ≤ k C k ( U ) ⊆ C j ( U ) if j ≤ k {\displaystyle {\begin{aligned}C^{k}(K)&\subseteq C_{c}^{k}(U)\subseteq C^{k}(U)\\C^{k}(K)&\subseteq C^{k}(L)&&{\text{ if }}K\subseteq L\\C^{k}(K)&\subseteq C^{j}(K)&&{\text{ if }}j\leq k\\C_{c}^{k}(U)&\subseteq C_{c}^{j}(U)&&{\text{ if }}j\leq k\\C^{k}(U)&\subseteq C^{j}(U)&&{\text{ if }}j\leq k\\\end{aligned}}}
Distributions on U are defined to be the continuous linear functionals on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} when this vector space is endowed with a particular topology called the canonical LF-topology. This topology is unfortunately not easy to define but it is nevertheless still possible to characterize distributions in a way so that no mention of the canonical LF-topology is made.
Proposition: If T is a linear functional on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} then the T is a distribution if and only if the following equivalent conditions are satisfied:
The above characterizations can be used to determine whether or not a linear functional is a distribution, but more advanced uses of distributions and test functions (such as applications to differential equations) is limited if no topologies are placed on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} and D ( U ) . {\displaystyle {\mathcal {D}}(U).} To define the space of distributions we must first define the canonical LF-topology, which in turn requires that several other locally convex topological vector spaces (TVSs) be defined first. First, a (non-normable) topology on C ∞ ( U ) {\displaystyle C^{\infty }(U)} will be defined, then every C ∞ ( K ) {\displaystyle C^{\infty }(K)} will be endowed with the subspace topology induced on it by C ∞ ( U ) , {\displaystyle C^{\infty }(U),} and finally the (non-metrizable) canonical LF-topology on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} will be defined. The space of distributions, being defined as the continuous dual space of C c ∞ ( U ) , {\displaystyle C_{c}^{\infty }(U),} is then endowed with the (non-metrizable) strong dual topology induced by C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} and the canonical LF-topology (this topology is a generalization of the usual operator norm induced topology that is placed on the continuous dual spaces of normed spaces). This finally permits consideration of more advanced notions such as convergence of distributions (both sequences and nets), various (sub)spaces of distributions, and operations on distributions, including extending differential equations to distributions.
Throughout, K {\displaystyle \mathbb {K} } will be any collection of compact subsets of U {\displaystyle U} such that (1) U = ⋃ K ∈ K K , {\textstyle U=\bigcup _{K\in \mathbb {K} }K,} and (2) for any compact K 1 , K 2 ⊆ U {\displaystyle K_{1},K_{2}\subseteq U} there exists some K ∈ K {\displaystyle K\in \mathbb {K} } such that K 1 ∪ K 2 ⊆ K . {\displaystyle K_{1}\cup K_{2}\subseteq K.} The most common choices for K {\displaystyle \mathbb {K} } are:
We make K {\displaystyle \mathbb {K} } into a directed set by defining K 1 ≤ K 2 {\displaystyle K_{1}\leq K_{2}} if and only if K 1 ⊆ K 2 . {\displaystyle K_{1}\subseteq K_{2}.} Note that although the definitions of the subsequently defined topologies explicitly reference K , {\displaystyle \mathbb {K} ,} in reality they do not depend on the choice of K ; {\displaystyle \mathbb {K} ;} that is, if K 1 {\displaystyle \mathbb {K} _{1}} and K 2 {\displaystyle \mathbb {K} _{2}} are any two such collections of compact subsets of U , {\displaystyle U,} then the topologies defined on C k ( U ) {\displaystyle C^{k}(U)} and C c k ( U ) {\displaystyle C_{c}^{k}(U)} by using K 1 {\displaystyle \mathbb {K} _{1}} in place of K {\displaystyle \mathbb {K} } are the same as those defined by using K 2 {\displaystyle \mathbb {K} _{2}} in place of K . {\displaystyle \mathbb {K} .}
We now introduce the seminorms that will define the topology on C k ( U ) . {\displaystyle C^{k}(U).} Different authors sometimes use different families of seminorms so we list the most common families below. However, the resulting topology is the same no matter which family is used.
(1) s p , K ( f ) := sup x 0 ∈ K | ∂ p f ( x 0 ) | (2) q i , K ( f ) := sup | p | ≤ i ( sup x 0 ∈ K | ∂ p f ( x 0 ) | ) = sup | p | ≤ i ( s p , K ( f ) ) (3) r i , K ( f ) := sup x 0 ∈ K | p | ≤ i | ∂ p f ( x 0 ) | (4) t i , K ( f ) := sup x 0 ∈ K ( ∑ | p | ≤ i | ∂ p f ( x 0 ) | ) {\displaystyle {\begin{alignedat}{4}{\text{ (1) }}\ &s_{p,K}(f)&&:=\sup _{x_{0}\in K}\left|\partial ^{p}f(x_{0})\right|\\[4pt]{\text{ (2) }}\ &q_{i,K}(f)&&:=\sup _{|p|\leq i}\left(\sup _{x_{0}\in K}\left|\partial ^{p}f(x_{0})\right|\right)=\sup _{|p|\leq i}\left(s_{p,K}(f)\right)\\[4pt]{\text{ (3) }}\ &r_{i,K}(f)&&:=\sup _{\stackrel {|p|\leq i}{x_{0}\in K}}\left|\partial ^{p}f(x_{0})\right|\\[4pt]{\text{ (4) }}\ &t_{i,K}(f)&&:=\sup _{x_{0}\in K}\left(\sum _{|p|\leq i}\left|\partial ^{p}f(x_{0})\right|\right)\end{alignedat}}}
All of the functions above are non-negative R {\displaystyle \mathbb {R} } -valued6 seminorms on C k ( U ) . {\displaystyle C^{k}(U).} As explained in this article, every set of seminorms on a vector space induces a locally convex vector topology.
Each of the following sets of seminorms A := { q i , K : K compact and i ∈ N satisfies 0 ≤ i ≤ k } B := { r i , K : K compact and i ∈ N satisfies 0 ≤ i ≤ k } C := { t i , K : K compact and i ∈ N satisfies 0 ≤ i ≤ k } D := { s p , K : K compact and p ∈ N n satisfies | p | ≤ k } {\displaystyle {\begin{alignedat}{4}A~:=\quad &\{q_{i,K}&&:\;K{\text{ compact and }}\;&&i\in \mathbb {N} {\text{ satisfies }}\;&&0\leq i\leq k\}\\B~:=\quad &\{r_{i,K}&&:\;K{\text{ compact and }}\;&&i\in \mathbb {N} {\text{ satisfies }}\;&&0\leq i\leq k\}\\C~:=\quad &\{t_{i,K}&&:\;K{\text{ compact and }}\;&&i\in \mathbb {N} {\text{ satisfies }}\;&&0\leq i\leq k\}\\D~:=\quad &\{s_{p,K}&&:\;K{\text{ compact and }}\;&&p\in \mathbb {N} ^{n}{\text{ satisfies }}\;&&|p|\leq k\}\end{alignedat}}} generate the same locally convex vector topology on C k ( U ) {\displaystyle C^{k}(U)} (so for example, the topology generated by the seminorms in A {\displaystyle A} is equal to the topology generated by those in C {\displaystyle C} ).
With this topology, C k ( U ) {\displaystyle C^{k}(U)} becomes a locally convex Fréchet space that is not normable. Every element of A ∪ B ∪ C ∪ D {\displaystyle A\cup B\cup C\cup D} is a continuous seminorm on C k ( U ) . {\displaystyle C^{k}(U).} Under this topology, a net ( f i ) i ∈ I {\displaystyle (f_{i})_{i\in I}} in C k ( U ) {\displaystyle C^{k}(U)} converges to f ∈ C k ( U ) {\displaystyle f\in C^{k}(U)} if and only if for every multi-index p {\displaystyle p} with | p | < k + 1 {\displaystyle |p|<k+1} and every compact K , {\displaystyle K,} the net of partial derivatives ( ∂ p f i ) i ∈ I {\displaystyle \left(\partial ^{p}f_{i}\right)_{i\in I}} converges uniformly to ∂ p f {\displaystyle \partial ^{p}f} on K . {\displaystyle K.} 7 For any k ∈ { 0 , 1 , 2 , … , ∞ } , {\displaystyle k\in \{0,1,2,\ldots ,\infty \},} any (von Neumann) bounded subset of C k + 1 ( U ) {\displaystyle C^{k+1}(U)} is a relatively compact subset of C k ( U ) . {\displaystyle C^{k}(U).} 8 In particular, a subset of C ∞ ( U ) {\displaystyle C^{\infty }(U)} is bounded if and only if it is bounded in C i ( U ) {\displaystyle C^{i}(U)} for all i ∈ N . {\displaystyle i\in \mathbb {N} .} 9 The space C k ( U ) {\displaystyle C^{k}(U)} is a Montel space if and only if k = ∞ . {\displaystyle k=\infty .} 10
The topology on C ∞ ( U ) {\displaystyle C^{\infty }(U)} is the superior limit of the subspace topologies induced on C ∞ ( U ) {\displaystyle C^{\infty }(U)} by the TVSs C i ( U ) {\displaystyle C^{i}(U)} as i ranges over the non-negative integers.11 A subset W {\displaystyle W} of C ∞ ( U ) {\displaystyle C^{\infty }(U)} is open in this topology if and only if there exists i ∈ N {\displaystyle i\in \mathbb {N} } such that W {\displaystyle W} is open when C ∞ ( U ) {\displaystyle C^{\infty }(U)} is endowed with the subspace topology induced on it by C i ( U ) . {\displaystyle C^{i}(U).}
If the family of compact sets K = { U ¯ 1 , U ¯ 2 , … } {\displaystyle \mathbb {K} =\left\{{\overline {U}}_{1},{\overline {U}}_{2},\ldots \right\}} satisfies U = ⋃ j = 1 ∞ U j {\textstyle U=\bigcup _{j=1}^{\infty }U_{j}} and U ¯ i ⊆ U i + 1 {\displaystyle {\overline {U}}_{i}\subseteq U_{i+1}} for all i , {\displaystyle i,} then a complete translation-invariant metric on C ∞ ( U ) {\displaystyle C^{\infty }(U)} can be obtained by taking a suitable countable Fréchet combination of any one of the above defining families of seminorms (A through D). For example, using the seminorms ( r i , K i ) i = 1 ∞ {\displaystyle (r_{i,K_{i}})_{i=1}^{\infty }} results in the metric d ( f , g ) := ∑ i = 1 ∞ 1 2 i r i , U ¯ i ( f − g ) 1 + r i , U ¯ i ( f − g ) = ∑ i = 1 ∞ 1 2 i sup | p | ≤ i , x ∈ U ¯ i | ∂ p ( f − g ) ( x ) | [ 1 + sup | p | ≤ i , x ∈ U ¯ i | ∂ p ( f − g ) ( x ) | ] . {\displaystyle d(f,g):=\sum _{i=1}^{\infty }{\frac {1}{2^{i}}}{\frac {r_{i,{\overline {U}}_{i}}(f-g)}{1+r_{i,{\overline {U}}_{i}}(f-g)}}=\sum _{i=1}^{\infty }{\frac {1}{2^{i}}}{\frac {\sup _{|p|\leq i,x\in {\overline {U}}_{i}}\left|\partial ^{p}(f-g)(x)\right|}{\left[1+\sup _{|p|\leq i,x\in {\overline {U}}_{i}}\left|\partial ^{p}(f-g)(x)\right|\right]}}.}
Often, it is easier to just consider seminorms (avoiding any metric) and use the tools of functional analysis.
As before, fix k ∈ { 0 , 1 , 2 , … , ∞ } . {\displaystyle k\in \{0,1,2,\ldots ,\infty \}.} Recall that if K {\displaystyle K} is any compact subset of U {\displaystyle U} then C k ( K ) ⊆ C k ( U ) . {\displaystyle C^{k}(K)\subseteq C^{k}(U).}
For any compact subset K ⊆ U , {\displaystyle K\subseteq U,} C k ( K ) {\displaystyle C^{k}(K)} is a closed subspace of the Fréchet space C k ( U ) {\displaystyle C^{k}(U)} and is thus also a Fréchet space. For all compact K , L ⊆ U {\displaystyle K,L\subseteq U} satisfying K ⊆ L , {\displaystyle K\subseteq L,} denote the inclusion map by In K L : C k ( K ) → C k ( L ) . {\displaystyle \operatorname {In} _{K}^{L}:C^{k}(K)\to C^{k}(L).} Then this map is a linear embedding of TVSs (that is, it is a linear map that is also a topological embedding) whose image (or "range") is closed in its codomain; said differently, the topology on C k ( K ) {\displaystyle C^{k}(K)} is identical to the subspace topology it inherits from C k ( L ) , {\displaystyle C^{k}(L),} and also C k ( K ) {\displaystyle C^{k}(K)} is a closed subset of C k ( L ) . {\displaystyle C^{k}(L).} The interior of C ∞ ( K ) {\displaystyle C^{\infty }(K)} relative to C ∞ ( U ) {\displaystyle C^{\infty }(U)} is empty.12
If k {\displaystyle k} is finite then C k ( K ) {\displaystyle C^{k}(K)} is a Banach space13 with a topology that can be defined by the norm r K ( f ) := sup | p | < k ( sup x 0 ∈ K | ∂ p f ( x 0 ) | ) . {\displaystyle r_{K}(f):=\sup _{|p|<k}\left(\sup _{x_{0}\in K}\left|\partial ^{p}f(x_{0})\right|\right).}
And when k = 2 , {\displaystyle k=2,} then C k ( K ) {\displaystyle \,C^{k}(K)} is even a Hilbert space.14 The space C ∞ ( K ) {\displaystyle C^{\infty }(K)} is a distinguished Schwartz Montel space so if C ∞ ( K ) ≠ { 0 } {\displaystyle C^{\infty }(K)\neq \{0\}} then it is not normable and thus not a Banach space (although like all other C k ( K ) , {\displaystyle C^{k}(K),} it is a Fréchet space).
The definition of C k ( K ) {\displaystyle C^{k}(K)} depends on U so we will let C k ( K ; U ) {\displaystyle C^{k}(K;U)} denote the topological space C k ( K ) , {\displaystyle C^{k}(K),} which by definition is a topological subspace of C k ( U ) . {\displaystyle C^{k}(U).} Suppose V {\displaystyle V} is an open subset of R n {\displaystyle \mathbb {R} ^{n}} containing U {\displaystyle U} and for any compact subset K ⊆ V , {\displaystyle K\subseteq V,} let C k ( K ; V ) {\displaystyle C^{k}(K;V)} is the vector subspace of C k ( V ) {\displaystyle C^{k}(V)} consisting of maps with support contained in K . {\displaystyle K.} Given f ∈ C c k ( U ) , {\displaystyle f\in C_{c}^{k}(U),} its trivial extension to V is by definition, the function I ( f ) := F : V → C {\displaystyle I(f):=F:V\to \mathbb {C} } defined by: F ( x ) = { f ( x ) x ∈ U , 0 otherwise , {\displaystyle F(x)={\begin{cases}f(x)&x\in U,\\0&{\text{otherwise}},\end{cases}}} so that F ∈ C k ( V ) . {\displaystyle F\in C^{k}(V).} Let I : C c k ( U ) → C k ( V ) {\displaystyle I:C_{c}^{k}(U)\to C^{k}(V)} denote the map that sends a function in C c k ( U ) {\displaystyle C_{c}^{k}(U)} to its trivial extension on V. This map is a linear injection and for every compact subset K ⊆ U {\displaystyle K\subseteq U} (where K {\displaystyle K} is also a compact subset of V {\displaystyle V} since K ⊆ U ⊆ V {\displaystyle K\subseteq U\subseteq V} ) we have I ( C k ( K ; U ) ) = C k ( K ; V ) and thus I ( C c k ( U ) ) ⊆ C c k ( V ) {\displaystyle {\begin{alignedat}{4}I\left(C^{k}(K;U)\right)&~=~C^{k}(K;V)\qquad {\text{ and thus }}\\I\left(C_{c}^{k}(U)\right)&~\subseteq ~C_{c}^{k}(V)\end{alignedat}}} If I is restricted to C k ( K ; U ) {\displaystyle C^{k}(K;U)} then the following induced linear map is a homeomorphism (and thus a TVS-isomorphism): C k ( K ; U ) → C k ( K ; V ) f ↦ I ( f ) {\displaystyle {\begin{alignedat}{4}\,&C^{k}(K;U)&&\to \,&&C^{k}(K;V)\\&f&&\mapsto \,&&I(f)\\\end{alignedat}}} and thus the next two maps (which like the previous map are defined by f ↦ I ( f ) {\displaystyle f\mapsto I(f)} ) are topological embeddings: C k ( K ; U ) → C k ( V ) , and C k ( K ; U ) → C c k ( V ) , {\displaystyle C^{k}(K;U)\to C^{k}(V),\qquad {\text{ and }}\qquad C^{k}(K;U)\to C_{c}^{k}(V),} (the topology on C c k ( V ) {\displaystyle C_{c}^{k}(V)} is the canonical LF topology, which is defined later). Using the injection I : C c k ( U ) → C k ( V ) {\displaystyle I:C_{c}^{k}(U)\to C^{k}(V)} the vector space C c k ( U ) {\displaystyle C_{c}^{k}(U)} is canonically identified with its image in C c k ( V ) ⊆ C k ( V ) {\displaystyle C_{c}^{k}(V)\subseteq C^{k}(V)} (however, if U ≠ V {\displaystyle U\neq V} then I : C c ∞ ( U ) → C c ∞ ( V ) {\displaystyle I:C_{c}^{\infty }(U)\to C_{c}^{\infty }(V)} is not a topological embedding when these spaces are endowed with their canonical LF topologies, although it is continuous).15 Because C k ( K ; U ) ⊆ C c k ( U ) , {\displaystyle C^{k}(K;U)\subseteq C_{c}^{k}(U),} through this identification, C k ( K ; U ) {\displaystyle C^{k}(K;U)} can also be considered as a subset of C k ( V ) . {\displaystyle C^{k}(V).} Importantly, the subspace topology C k ( K ; U ) {\displaystyle C^{k}(K;U)} inherits from C k ( U ) {\displaystyle C^{k}(U)} (when it is viewed as a subset of C k ( U ) {\displaystyle C^{k}(U)} ) is identical to the subspace topology that it inherits from C k ( V ) {\displaystyle C^{k}(V)} (when C k ( K ; U ) {\displaystyle C^{k}(K;U)} is viewed instead as a subset of C k ( V ) {\displaystyle C^{k}(V)} via the identification). Thus the topology on C k ( K ; U ) {\displaystyle C^{k}(K;U)} is independent of the open subset U of R n {\displaystyle \mathbb {R} ^{n}} that contains K.16 This justifies the practice of writing C k ( K ) {\displaystyle C^{k}(K)} instead of C k ( K ; U ) . {\displaystyle C^{k}(K;U).}
See also: LF-space and Topology of uniform convergence
Recall that C c k ( U ) {\displaystyle C_{c}^{k}(U)} denote all those functions in C k ( U ) {\displaystyle C^{k}(U)} that have compact support in U , {\displaystyle U,} where note that C c k ( U ) {\displaystyle C_{c}^{k}(U)} is the union of all C k ( K ) {\displaystyle C^{k}(K)} as K ranges over K . {\displaystyle \mathbb {K} .} Moreover, for every k, C c k ( U ) {\displaystyle C_{c}^{k}(U)} is a dense subset of C k ( U ) . {\displaystyle C^{k}(U).} The special case when k = ∞ {\displaystyle k=\infty } gives us the space of test functions.
This section defines the canonical LF topology as a direct limit. It is also possible to define this topology in terms of its neighborhoods of the origin, which is described afterwards.
For any two sets K and L, we declare that K ≤ L {\displaystyle K\leq L} if and only if K ⊆ L , {\displaystyle K\subseteq L,} which in particular makes the collection K {\displaystyle \mathbb {K} } of compact subsets of U into a directed set (we say that such a collection is directed by subset inclusion). For all compact K , L ⊆ U {\displaystyle K,L\subseteq U} satisfying K ⊆ L , {\displaystyle K\subseteq L,} there are inclusion maps In K L : C k ( K ) → C k ( L ) and In K U : C k ( K ) → C c k ( U ) . {\displaystyle \operatorname {In} _{K}^{L}:C^{k}(K)\to C^{k}(L)\quad {\text{and}}\quad \operatorname {In} _{K}^{U}:C^{k}(K)\to C_{c}^{k}(U).}
Recall from above that the map In K L : C k ( K ) → C k ( L ) {\displaystyle \operatorname {In} _{K}^{L}:C^{k}(K)\to C^{k}(L)} is a topological embedding. The collection of maps { In K L : K , L ∈ K and K ⊆ L } {\displaystyle \left\{\operatorname {In} _{K}^{L}\;:\;K,L\in \mathbb {K} \;{\text{ and }}\;K\subseteq L\right\}} forms a direct system in the category of locally convex topological vector spaces that is directed by K {\displaystyle \mathbb {K} } (under subset inclusion). This system's direct limit (in the category of locally convex TVSs) is the pair ( C c k ( U ) , In ∙ U ) {\displaystyle (C_{c}^{k}(U),\operatorname {In} _{\bullet }^{U})} where In ∙ U := ( In K U ) K ∈ K {\displaystyle \operatorname {In} _{\bullet }^{U}:=\left(\operatorname {In} _{K}^{U}\right)_{K\in \mathbb {K} }} are the natural inclusions and where C c k ( U ) {\displaystyle C_{c}^{k}(U)} is now endowed with the (unique) strongest locally convex topology making all of the inclusion maps In ∙ U = ( In K U ) K ∈ K {\displaystyle \operatorname {In} _{\bullet }^{U}=(\operatorname {In} _{K}^{U})_{K\in \mathbb {K} }} continuous.
If U {\displaystyle {\cal {U}}} is a convex subset of C c k ( U ) , {\displaystyle C_{c}^{k}(U),} then U {\displaystyle {\cal {U}}} is a neighborhood of the origin in the canonical LF topology if and only if it satisfies the following condition:
Note that any convex set satisfying this condition is necessarily absorbing in C c k ( U ) . {\displaystyle C_{c}^{k}(U).} Since the topology of any topological vector space is translation-invariant, any TVS-topology is completely determined by the set of neighborhood of the origin. This means that one could actually define the canonical LF topology by declaring that a convex balanced subset U is a neighborhood of the origin if and only if it satisfies condition CN.
A linear differential operator in U with smooth coefficients is a sum P := ∑ α ∈ N n c α ∂ α {\displaystyle P:=\sum _{\alpha \in \mathbb {N} ^{n}}c_{\alpha }\partial ^{\alpha }} where c α ∈ C ∞ ( U ) {\displaystyle c_{\alpha }\in C^{\infty }(U)} and all but finitely many of c α {\displaystyle c_{\alpha }} are identically 0. The integer sup { | α | : c α ≠ 0 } {\displaystyle \sup\{|\alpha |:c_{\alpha }\neq 0\}} is called the order of the differential operator P . {\displaystyle P.} If P {\displaystyle P} is a linear differential operator of order k then it induces a canonical linear map C k ( U ) → C 0 ( U ) {\displaystyle C^{k}(U)\to C^{0}(U)} defined by ϕ ↦ P ϕ , {\displaystyle \phi \mapsto P\phi ,} where we shall reuse notation and also denote this map by P . {\displaystyle P.} 17
For any 1 ≤ k ≤ ∞ , {\displaystyle 1\leq k\leq \infty ,} the canonical LF topology on C c k ( U ) {\displaystyle C_{c}^{k}(U)} is the weakest locally convex TVS topology making all linear differential operators in U {\displaystyle U} of order < k + 1 {\displaystyle \,<k+1} into continuous maps from C c k ( U ) {\displaystyle C_{c}^{k}(U)} into C c 0 ( U ) . {\displaystyle C_{c}^{0}(U).} 18
One benefit of defining the canonical LF topology as the direct limit of a direct system is that we may immediately use the universal property of direct limits. Another benefit is that we can use well-known results from category theory to deduce that the canonical LF topology is actually independent of the particular choice of the directed collection K {\displaystyle \mathbb {K} } of compact sets. And by considering different collections K {\displaystyle \mathbb {K} } (in particular, those K {\displaystyle \mathbb {K} } mentioned at the beginning of this article), we may deduce different properties of this topology. In particular, we may deduce that the canonical LF topology makes C c k ( U ) {\displaystyle C_{c}^{k}(U)} into a Hausdorff locally convex strict LF-space (and also a strict LB-space if k ≠ ∞ {\displaystyle k\neq \infty } ), which of course is the reason why this topology is called "the canonical LF topology" (see this footnote for more details).19
From the universal property of direct limits, we know that if u : C c k ( U ) → Y {\displaystyle u:C_{c}^{k}(U)\to Y} is a linear map into a locally convex space Y (not necessarily Hausdorff), then u is continuous if and only if u is bounded if and only if for every K ∈ K , {\displaystyle K\in \mathbb {K} ,} the restriction of u to C k ( K ) {\displaystyle C^{k}(K)} is continuous (or bounded).2021
Suppose V is an open subset of R n {\displaystyle \mathbb {R} ^{n}} containing U . {\displaystyle U.} Let I : C c k ( U ) → C c k ( V ) {\displaystyle I:C_{c}^{k}(U)\to C_{c}^{k}(V)} denote the map that sends a function in C c k ( U ) {\displaystyle C_{c}^{k}(U)} to its trivial extension on V (which was defined above). This map is a continuous linear map.22 If (and only if) U ≠ V {\displaystyle U\neq V} then I ( C c ∞ ( U ) ) {\displaystyle I\left(C_{c}^{\infty }(U)\right)} is not a dense subset of C c ∞ ( V ) {\displaystyle C_{c}^{\infty }(V)} and I : C c ∞ ( U ) → C c ∞ ( V ) {\displaystyle I:C_{c}^{\infty }(U)\to C_{c}^{\infty }(V)} is not a topological embedding.23 Consequently, if U ≠ V {\displaystyle U\neq V} then the transpose of I : C c ∞ ( U ) → C c ∞ ( V ) {\displaystyle I:C_{c}^{\infty }(U)\to C_{c}^{\infty }(V)} is neither one-to-one nor onto.24
A subset B ⊆ C c k ( U ) {\displaystyle B\subseteq C_{c}^{k}(U)} is bounded in C c k ( U ) {\displaystyle C_{c}^{k}(U)} if and only if there exists some K ∈ K {\displaystyle K\in \mathbb {K} } such that B ⊆ C k ( K ) {\displaystyle B\subseteq C^{k}(K)} and B {\displaystyle B} is a bounded subset of C k ( K ) . {\displaystyle C^{k}(K).} 25 Moreover, if K ⊆ U {\displaystyle K\subseteq U} is compact and S ⊆ C k ( K ) {\displaystyle S\subseteq C^{k}(K)} then S {\displaystyle S} is bounded in C k ( K ) {\displaystyle C^{k}(K)} if and only if it is bounded in C k ( U ) . {\displaystyle C^{k}(U).} For any 0 ≤ k ≤ ∞ , {\displaystyle 0\leq k\leq \infty ,} any bounded subset of C c k + 1 ( U ) {\displaystyle C_{c}^{k+1}(U)} (resp. C k + 1 ( U ) {\displaystyle C^{k+1}(U)} ) is a relatively compact subset of C c k ( U ) {\displaystyle C_{c}^{k}(U)} (resp. C k ( U ) {\displaystyle C^{k}(U)} ), where ∞ + 1 = ∞ . {\displaystyle \infty +1=\infty .} 26
For all compact K ⊆ U , {\displaystyle K\subseteq U,} the interior of C k ( K ) {\displaystyle C^{k}(K)} in C c k ( U ) {\displaystyle C_{c}^{k}(U)} is empty so that C c k ( U ) {\displaystyle C_{c}^{k}(U)} is of the first category in itself. It follows from Baire's theorem that C c k ( U ) {\displaystyle C_{c}^{k}(U)} is not metrizable and thus also not normable (see this footnote27 for an explanation of how the non-metrizable space C c k ( U ) {\displaystyle C_{c}^{k}(U)} can be complete even though it does not admit a metric). The fact that C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} is a nuclear Montel space makes up for the non-metrizability of C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} (see this footnote for a more detailed explanation).28
Using the universal property of direct limits and the fact that the natural inclusions In K L : C k ( K ) → C k ( L ) {\displaystyle \operatorname {In} _{K}^{L}:C^{k}(K)\to C^{k}(L)} are all topological embedding, one may show that all of the maps In K U : C k ( K ) → C c k ( U ) {\displaystyle \operatorname {In} _{K}^{U}:C^{k}(K)\to C_{c}^{k}(U)} are also topological embeddings. Said differently, the topology on C k ( K ) {\displaystyle C^{k}(K)} is identical to the subspace topology that it inherits from C c k ( U ) , {\displaystyle C_{c}^{k}(U),} where recall that C k ( K ) {\displaystyle C^{k}(K)} 's topology was defined to be the subspace topology induced on it by C k ( U ) . {\displaystyle C^{k}(U).} In particular, both C c k ( U ) {\displaystyle C_{c}^{k}(U)} and C k ( U ) {\displaystyle C^{k}(U)} induces the same subspace topology on C k ( K ) . {\displaystyle C^{k}(K).} However, this does not imply that the canonical LF topology on C c k ( U ) {\displaystyle C_{c}^{k}(U)} is equal to the subspace topology induced on C c k ( U ) {\displaystyle C_{c}^{k}(U)} by C k ( U ) {\displaystyle C^{k}(U)} ; these two topologies on C c k ( U ) {\displaystyle C_{c}^{k}(U)} are in fact never equal to each other since the canonical LF topology is never metrizable while the subspace topology induced on it by C k ( U ) {\displaystyle C^{k}(U)} is metrizable (since recall that C k ( U ) {\displaystyle C^{k}(U)} is metrizable). The canonical LF topology on C c k ( U ) {\displaystyle C_{c}^{k}(U)} is actually strictly finer than the subspace topology that it inherits from C k ( U ) {\displaystyle C^{k}(U)} (thus the natural inclusion C c k ( U ) → C k ( U ) {\displaystyle C_{c}^{k}(U)\to C^{k}(U)} is continuous but not a topological embedding).29
Indeed, the canonical LF topology is so fine that if C c ∞ ( U ) → X {\displaystyle C_{c}^{\infty }(U)\to X} denotes some linear map that is a "natural inclusion" (such as C c ∞ ( U ) → C k ( U ) , {\displaystyle C_{c}^{\infty }(U)\to C^{k}(U),} or C c ∞ ( U ) → L p ( U ) , {\displaystyle C_{c}^{\infty }(U)\to L^{p}(U),} or other maps discussed below) then this map will typically be continuous, which (as is explained below) is ultimately the reason why locally integrable functions, Radon measures, etc. all induce distributions (via the transpose of such a "natural inclusion"). Said differently, the reason why there are so many different ways of defining distributions from other spaces ultimately stems from how very fine the canonical LF topology is. Moreover, since distributions are just continuous linear functionals on C c ∞ ( U ) , {\displaystyle C_{c}^{\infty }(U),} the fine nature of the canonical LF topology means that more linear functionals on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} end up being continuous ("more" means as compared to a coarser topology that we could have placed on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} such as for instance, the subspace topology induced by some C k ( U ) , {\displaystyle C^{k}(U),} which although it would have made C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} metrizable, it would have also resulted in fewer linear functionals on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} being continuous and thus there would have been fewer distributions; moreover, this particular coarser topology also has the disadvantage of not making C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} into a complete TVS30).
See also: Continuous linear functional
As discussed earlier, continuous linear functionals on a C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} are known as distributions on U. Thus the set of all distributions on U is the continuous dual space of C c ∞ ( U ) , {\displaystyle C_{c}^{\infty }(U),} which when endowed with the strong dual topology is denoted by D ′ ( U ) . {\displaystyle {\mathcal {D}}^{\prime }(U).}
We have the canonical duality pairing between a distribution T on U and a test function f ∈ C c ∞ ( U ) , {\displaystyle f\in C_{c}^{\infty }(U),} which is denoted using angle brackets by { D ′ ( U ) × C c ∞ ( U ) → R ( T , f ) ↦ ⟨ T , f ⟩ := T ( f ) {\displaystyle {\begin{cases}{\mathcal {D}}^{\prime }(U)\times C_{c}^{\infty }(U)\to \mathbb {R} \\(T,f)\mapsto \langle T,f\rangle :=T(f)\end{cases}}}
One interprets this notation as the distribution T acting on the test function f {\displaystyle f} to give a scalar, or symmetrically as the test function f {\displaystyle f} acting on the distribution T.
Proposition. If T is a linear functional on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} then the following are equivalent:
See also: Strong dual space, Polar topology, Dual topology, and Dual system
The topology of uniform convergence on bounded subsets is also called the strong dual topology.40 This topology is chosen because it is with this topology that D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} becomes a nuclear Montel space and it is with this topology that the kernels theorem of Schwartz holds.41 No matter what dual topology is placed on D ′ ( U ) , {\displaystyle {\mathcal {D}}^{\prime }(U),} 42 a sequence of distributions converges in this topology if and only if it converges pointwise (although this need not be true of a net). No matter which topology is chosen, D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} will be a non-metrizable, locally convex topological vector space. The space D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} is separable43 and has the strong Pytkeev property44 but it is neither a k-space45 nor a sequential space,46 which in particular implies that it is not metrizable and also that its topology can not be defined using only sequences.
The canonical LF topology makes C c k ( U ) {\displaystyle C_{c}^{k}(U)} into a complete distinguished strict LF-space (and a strict LB-space if and only if k ≠ ∞ {\displaystyle k\neq \infty } 47), which implies that C c k ( U ) {\displaystyle C_{c}^{k}(U)} is a meager subset of itself.48 Furthermore, C c k ( U ) , {\displaystyle C_{c}^{k}(U),} as well as its strong dual space, is a complete Hausdorff locally convex barrelled bornological Mackey space. The strong dual of C c k ( U ) {\displaystyle C_{c}^{k}(U)} is a Fréchet space if and only if k ≠ ∞ {\displaystyle k\neq \infty } so in particular, the strong dual of C c ∞ ( U ) , {\displaystyle C_{c}^{\infty }(U),} which is the space D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} of distributions on U, is not metrizable (note that the weak-* topology on D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} also is not metrizable and moreover, it further lacks almost all of the nice properties that the strong dual topology gives D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} ).
The three spaces C c ∞ ( U ) , {\displaystyle C_{c}^{\infty }(U),} C ∞ ( U ) , {\displaystyle C^{\infty }(U),} and the Schwartz space S ( R n ) , {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n}),} as well as the strong duals of each of these three spaces, are complete nuclear49 Montel50 bornological spaces, which implies that all six of these locally convex spaces are also paracompact51 reflexive barrelled Mackey spaces. The spaces C ∞ ( U ) {\displaystyle C^{\infty }(U)} and S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} are both distinguished Fréchet spaces. Moreover, both C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} and S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} are Schwartz TVSs.
The strong dual spaces of C ∞ ( U ) {\displaystyle C^{\infty }(U)} and S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} are sequential spaces but not Fréchet-Urysohn spaces.52 Moreover, neither the space of test functions C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} nor its strong dual D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} is a sequential space (not even an Ascoli space),5354 which in particular implies that their topologies can not be defined entirely in terms of convergent sequences.
A sequence ( f i ) i = 1 ∞ {\displaystyle \left(f_{i}\right)_{i=1}^{\infty }} in C c k ( U ) {\displaystyle C_{c}^{k}(U)} converges in C c k ( U ) {\displaystyle C_{c}^{k}(U)} if and only if there exists some K ∈ K {\displaystyle K\in \mathbb {K} } such that C k ( K ) {\displaystyle C^{k}(K)} contains this sequence and this sequence converges in C k ( K ) {\displaystyle C^{k}(K)} ; equivalently, it converges if and only if the following two conditions hold:55
Neither the space C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} nor its strong dual D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} is a sequential space,5657 and consequently, their topologies can not be defined entirely in terms of convergent sequences. For this reason, the above characterization of when a sequence converges is not enough to define the canonical LF topology on C c ∞ ( U ) . {\displaystyle C_{c}^{\infty }(U).} The same can be said of the strong dual topology on D ′ ( U ) . {\displaystyle {\mathcal {D}}^{\prime }(U).}
Nevertheless, sequences do characterize many important properties, as we now discuss. It is known that in the dual space of any Montel space, a sequence converges in the strong dual topology if and only if it converges in the weak* topology,58 which in particular, is the reason why a sequence of distributions converges (in the strong dual topology) if and only if it converges pointwise (this leads many authors to use pointwise convergence to actually define the convergence of a sequence of distributions; this is fine for sequences but it does not extend to the convergence of nets of distributions since a net may converge pointwise but fail to converge in the strong dual topology).
Sequences characterize continuity of linear maps valued in locally convex space. Suppose X is a locally convex bornological space (such as any of the six TVSs mentioned earlier). Then a linear map F : X → Y {\displaystyle F:X\to Y} into a locally convex space Y is continuous if and only if it maps null sequences59 in X to bounded subsets of Y.60 More generally, such a linear map F : X → Y {\displaystyle F:X\to Y} is continuous if and only if it maps Mackey convergent null sequences61 to bounded subsets of Y . {\displaystyle Y.} So in particular, if a linear map F : X → Y {\displaystyle F:X\to Y} into a locally convex space is sequentially continuous at the origin then it is continuous.62 However, this does not necessarily extend to non-linear maps and/or to maps valued in topological spaces that are not locally convex TVSs.
For every k ∈ { 0 , 1 , … , ∞ } , C c ∞ ( U ) {\displaystyle k\in \{0,1,\ldots ,\infty \},C_{c}^{\infty }(U)} is sequentially dense in C c k ( U ) . {\displaystyle C_{c}^{k}(U).} 63 Furthermore, { D ϕ : ϕ ∈ C c ∞ ( U ) } {\displaystyle \{D_{\phi }:\phi \in C_{c}^{\infty }(U)\}} is a sequentially dense subset of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} (with its strong dual topology)64 and also a sequentially dense subset of the strong dual space of C ∞ ( U ) . {\displaystyle C^{\infty }(U).} 65
Main article: Limit of distributions
A sequence of distributions ( T i ) i = 1 ∞ {\displaystyle (T_{i})_{i=1}^{\infty }} converges with respect to the weak-* topology on D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} to a distribution T if and only if ⟨ T i , f ⟩ → ⟨ T , f ⟩ {\displaystyle \langle T_{i},f\rangle \to \langle T,f\rangle } for every test function f ∈ D ( U ) . {\displaystyle f\in {\mathcal {D}}(U).} For example, if f m : R → R {\displaystyle f_{m}:\mathbb {R} \to \mathbb {R} } is the function f m ( x ) = { m if x ∈ [ 0 , 1 m ] 0 otherwise {\displaystyle f_{m}(x)={\begin{cases}m&{\text{ if }}x\in [0,{\frac {1}{m}}]\\0&{\text{ otherwise }}\end{cases}}} and T m {\displaystyle T_{m}} is the distribution corresponding to f m , {\displaystyle f_{m},} then ⟨ T m , f ⟩ = m ∫ 0 1 m f ( x ) d x → f ( 0 ) = ⟨ δ , f ⟩ {\displaystyle \langle T_{m},f\rangle =m\int _{0}^{\frac {1}{m}}f(x)\,dx\to f(0)=\langle \delta ,f\rangle } as m → ∞ , {\displaystyle m\to \infty ,} so T m → δ {\displaystyle T_{m}\to \delta } in D ′ ( R ) . {\displaystyle {\mathcal {D}}^{\prime }(\mathbb {R} ).} Thus, for large m , {\displaystyle m,} the function f m {\displaystyle f_{m}} can be regarded as an approximation of the Dirac delta distribution.
Main article: Transpose of a linear map
Operations on distributions and spaces of distributions are often defined by means of the transpose of a linear operator. This is because the transpose allows for a unified presentation of the many definitions in the theory of distributions and also because its properties are well known in functional analysis.66 For instance, the well-known Hermitian adjoint of a linear operator between Hilbert spaces is just the operator's transpose (but with the Riesz representation theorem used to identify each Hilbert space with its continuous dual space). In general the transpose of a continuous linear map A : X → Y {\displaystyle A:X\to Y} is the linear map t A : Y ′ → X ′ defined by t A ( y ′ ) := y ′ ∘ A , {\displaystyle {}^{t}A:Y'\to X'\qquad {\text{ defined by }}\qquad {}^{t}A(y'):=y'\circ A,} or equivalently, it is the unique map satisfying ⟨ y ′ , A ( x ) ⟩ = ⟨ t A ( y ′ ) , x ⟩ {\displaystyle \langle y',A(x)\rangle =\left\langle {}^{t}A(y'),x\right\rangle } for all x ∈ X {\displaystyle x\in X} and all y ′ ∈ Y ′ {\displaystyle y'\in Y'} (the prime symbol in y ′ {\displaystyle y'} does not denote a derivative of any kind; it merely indicates that y ′ {\displaystyle y'} is an element of the continuous dual space Y ′ {\displaystyle Y'} ). Since A {\displaystyle A} is continuous, the transpose t A : Y ′ → X ′ {\displaystyle {}^{t}A:Y'\to X'} is also continuous when both duals are endowed with their respective strong dual topologies; it is also continuous when both duals are endowed with their respective weak* topologies (see the articles polar topology and dual system for more details).
In the context of distributions, the characterization of the transpose can be refined slightly. Let A : D ( U ) → D ( U ) {\displaystyle A:{\mathcal {D}}(U)\to {\mathcal {D}}(U)} be a continuous linear map. Then by definition, the transpose of A {\displaystyle A} is the unique linear operator A t : D ′ ( U ) → D ′ ( U ) {\displaystyle A^{t}:{\mathcal {D}}'(U)\to {\mathcal {D}}'(U)} that satisfies: ⟨ t A ( T ) , ϕ ⟩ = ⟨ T , A ( ϕ ) ⟩ for all ϕ ∈ D ( U ) and all T ∈ D ′ ( U ) . {\displaystyle \langle {}^{t}A(T),\phi \rangle =\langle T,A(\phi )\rangle \quad {\text{ for all }}\phi \in {\mathcal {D}}(U){\text{ and all }}T\in {\mathcal {D}}'(U).}
Since D ( U ) {\displaystyle {\mathcal {D}}(U)} is dense in D ′ ( U ) {\displaystyle {\mathcal {D}}'(U)} (here, D ( U ) {\displaystyle {\mathcal {D}}(U)} actually refers to the set of distributions { D ψ : ψ ∈ D ( U ) } {\displaystyle \left\{D_{\psi }:\psi \in {\mathcal {D}}(U)\right\}} ) it is sufficient that the defining equality hold for all distributions of the form T = D ψ {\displaystyle T=D_{\psi }} where ψ ∈ D ( U ) . {\displaystyle \psi \in {\mathcal {D}}(U).} Explicitly, this means that a continuous linear map B : D ′ ( U ) → D ′ ( U ) {\displaystyle B:{\mathcal {D}}'(U)\to {\mathcal {D}}'(U)} is equal to t A {\displaystyle {}^{t}A} if and only if the condition below holds: ⟨ B ( D ψ ) , ϕ ⟩ = ⟨ t A ( D ψ ) , ϕ ⟩ for all ϕ , ψ ∈ D ( U ) {\displaystyle \langle B(D_{\psi }),\phi \rangle =\langle {}^{t}A(D_{\psi }),\phi \rangle \quad {\text{ for all }}\phi ,\psi \in {\mathcal {D}}(U)} where the right hand side equals ⟨ t A ( D ψ ) , ϕ ⟩ = ⟨ D ψ , A ( ϕ ) ⟩ = ⟨ ψ , A ( ϕ ) ⟩ = ∫ U ψ ⋅ A ( ϕ ) d x . {\displaystyle \langle {}^{t}A(D_{\psi }),\phi \rangle =\langle D_{\psi },A(\phi )\rangle =\langle \psi ,A(\phi )\rangle =\int _{U}\psi \cdot A(\phi )\,dx.}
Let V ⊆ U {\displaystyle V\subseteq U} be open subsets of R n . {\displaystyle \mathbb {R} ^{n}.} Every function f ∈ D ( V ) {\displaystyle f\in {\mathcal {D}}(V)} can be extended by zero from its domain V {\displaystyle V} to a function on U {\displaystyle U} by setting it equal to 0 {\displaystyle 0} on the complement U ∖ V . {\displaystyle U\setminus V.} This extension is a smooth compactly supported function called the trivial extension of f {\displaystyle f} to U {\displaystyle U} and it will be denoted by E V U ( f ) . {\displaystyle E_{VU}(f).} This assignment f ↦ E V U ( f ) {\displaystyle f\mapsto E_{VU}(f)} defines the trivial extension operator E V U : D ( V ) → D ( U ) , {\displaystyle E_{VU}:{\mathcal {D}}(V)\to {\mathcal {D}}(U),} which is a continuous injective linear map. It is used to canonically identify D ( V ) {\displaystyle {\mathcal {D}}(V)} as a vector subspace of D ( U ) {\displaystyle {\mathcal {D}}(U)} (although not as a topological subspace). Its transpose (explained here) ρ V U := t E V U : D ′ ( U ) → D ′ ( V ) , {\displaystyle \rho _{VU}:={}^{t}E_{VU}:{\mathcal {D}}'(U)\to {\mathcal {D}}'(V),} is called the restriction to V {\displaystyle V} of distributions in U {\displaystyle U} 67 and as the name suggests, the image ρ V U ( T ) {\displaystyle \rho _{VU}(T)} of a distribution T ∈ D ′ ( U ) {\displaystyle T\in {\mathcal {D}}'(U)} under this map is a distribution on V {\displaystyle V} called the restriction of T {\displaystyle T} to V . {\displaystyle V.} The defining condition of the restriction ρ V U ( T ) {\displaystyle \rho _{VU}(T)} is: ⟨ ρ V U T , ϕ ⟩ = ⟨ T , E V U ϕ ⟩ for all ϕ ∈ D ( V ) . {\displaystyle \langle \rho _{VU}T,\phi \rangle =\langle T,E_{VU}\phi \rangle \quad {\text{ for all }}\phi \in {\mathcal {D}}(V).} If V ≠ U {\displaystyle V\neq U} then the (continuous injective linear) trivial extension map E V U : D ( V ) → D ( U ) {\displaystyle E_{VU}:{\mathcal {D}}(V)\to {\mathcal {D}}(U)} is not a topological embedding (in other words, if this linear injection was used to identify D ( V ) {\displaystyle {\mathcal {D}}(V)} as a subset of D ( U ) {\displaystyle {\mathcal {D}}(U)} then D ( V ) {\displaystyle {\mathcal {D}}(V)} 's topology would strictly finer than the subspace topology that D ( U ) {\displaystyle {\mathcal {D}}(U)} induces on it; importantly, it would not be a topological subspace since that requires equality of topologies) and its range is also not dense in its codomain D ( U ) . {\displaystyle {\mathcal {D}}(U).} 68 Consequently, if V ≠ U {\displaystyle V\neq U} then the restriction mapping is neither injective nor surjective.69 A distribution S ∈ D ′ ( V ) {\displaystyle S\in {\mathcal {D}}'(V)} is said to be extendible to U if it belongs to the range of the transpose of E V U {\displaystyle E_{VU}} and it is called extendible if it is extendable to R n . {\displaystyle \mathbb {R} ^{n}.} 70
Unless U = V , {\displaystyle U=V,} the restriction to V {\displaystyle V} is neither injective nor surjective.
For all 0 < k < ∞ {\displaystyle 0<k<\infty } and all 1 < p < ∞ , {\displaystyle 1<p<\infty ,} all of the following canonical injections are continuous and have an image/range that is a dense subset of their codomain:7172 C c ∞ ( U ) → C c k ( U ) → C c 0 ( U ) → L c ∞ ( U ) → L c p + 1 ( U ) → L c p ( U ) → L c 1 ( U ) ↓ ↓ ↓ C ∞ ( U ) → C k ( U ) → C 0 ( U ) {\displaystyle {\begin{matrix}C_{c}^{\infty }(U)&\to &C_{c}^{k}(U)&\to &C_{c}^{0}(U)&\to &L_{c}^{\infty }(U)&\to &L_{c}^{p+1}(U)&\to &L_{c}^{p}(U)&\to &L_{c}^{1}(U)\\\downarrow &&\downarrow &&\downarrow &&&&&&&&&&\\C^{\infty }(U)&\to &C^{k}(U)&\to &C^{0}(U)&&&&&&&&&&\end{matrix}}} where the topologies on the LB-spaces L c p ( U ) {\displaystyle L_{c}^{p}(U)} are the canonical LF topologies as defined below (so in particular, they are not the usual norm topologies). The range of each of the maps above (and of any composition of the maps above) is dense in the codomain. Indeed, C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} is even sequentially dense in every C c k ( U ) . {\displaystyle C_{c}^{k}(U).} 73 For every 1 ≤ p ≤ ∞ , {\displaystyle 1\leq p\leq \infty ,} the canonical inclusion C c ∞ ( U ) → L p ( U ) {\displaystyle C_{c}^{\infty }(U)\to L^{p}(U)} into the normed space L p ( U ) {\displaystyle L^{p}(U)} (here L p ( U ) {\displaystyle L^{p}(U)} has its usual norm topology) is a continuous linear injection and the range of this injection is dense in its codomain if and only if p ≠ ∞ {\displaystyle p\neq \infty } .74
Suppose that X {\displaystyle X} is one of the LF-spaces C c k ( U ) {\displaystyle C_{c}^{k}(U)} (for k ∈ { 0 , 1 , … , ∞ } {\displaystyle k\in \{0,1,\ldots ,\infty \}} ) or LB-spaces L c p ( U ) {\displaystyle L_{c}^{p}(U)} (for 1 ≤ p ≤ ∞ {\displaystyle 1\leq p\leq \infty } ) or normed spaces L p ( U ) {\displaystyle L^{p}(U)} (for 1 ≤ p < ∞ {\displaystyle 1\leq p<\infty } ).75 Because the canonical injection In X : C c ∞ ( U ) → X {\displaystyle \operatorname {In} _{X}:C_{c}^{\infty }(U)\to X} is a continuous injection whose image is dense in the codomain, this map's transpose t In X : X b ′ → D ′ ( U ) = ( C c ∞ ( U ) ) b ′ {\displaystyle {}^{t}\operatorname {In} _{X}:X'_{b}\to {\mathcal {D}}'(U)=\left(C_{c}^{\infty }(U)\right)'_{b}} is a continuous injection. This injective transpose map thus allows the continuous dual space X ′ {\displaystyle X'} of X {\displaystyle X} to be identified with a certain vector subspace of the space D ′ ( U ) {\displaystyle {\mathcal {D}}'(U)} of all distributions (specifically, it is identified with the image of this transpose map). This continuous transpose map is not necessarily a TVS-embedding so the topology that this map transfers from its domain to the image Im ( t In X ) {\displaystyle \operatorname {Im} \left({}^{t}\operatorname {In} _{X}\right)} is finer than the subspace topology that this space inherits from D ′ ( U ) . {\displaystyle {\mathcal {D}}^{\prime }(U).} A linear subspace of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} carrying a locally convex topology that is finer than the subspace topology induced by D ′ ( U ) = ( C c ∞ ( U ) ) b ′ {\displaystyle {\mathcal {D}}^{\prime }(U)=\left(C_{c}^{\infty }(U)\right)_{b}^{\prime }} is called a space of distributions.76 Almost all of the spaces of distributions mentioned in this article arise in this way (e.g. tempered distribution, restrictions, distributions of order ≤ {\displaystyle \leq } some integer, distributions induced by a positive Radon measure, distributions induced by an L p {\displaystyle L^{p}} -function, etc.) and any representation theorem about the dual space of X may, through the transpose t In X : X b ′ → D ′ ( U ) , {\displaystyle {}^{t}\operatorname {In} _{X}:X'_{b}\to {\mathcal {D}}^{\prime }(U),} be transferred directly to elements of the space Im ( t In X ) . {\displaystyle \operatorname {Im} \left({}^{t}\operatorname {In} _{X}\right).}
Given 1 ≤ p ≤ ∞ , {\displaystyle 1\leq p\leq \infty ,} the vector space L c p ( U ) {\displaystyle L_{c}^{p}(U)} of compactly supported L p {\displaystyle L^{p}} functions on U {\displaystyle U} and its topology are defined as direct limits of the spaces L c p ( K ) {\displaystyle L_{c}^{p}(K)} in a manner analogous to how the canonical LF-topologies on C c k ( U ) {\displaystyle C_{c}^{k}(U)} were defined. For any compact K ⊆ U , {\displaystyle K\subseteq U,} let L p ( K ) {\displaystyle L^{p}(K)} denote the set of all element in L p ( U ) {\displaystyle L^{p}(U)} (which recall are equivalence class of Lebesgue measurable L p {\displaystyle L^{p}} functions on U {\displaystyle U} ) having a representative f {\displaystyle f} whose support (which recall is the closure of { u ∈ U : f ( x ) ≠ 0 } {\displaystyle \{u\in U:f(x)\neq 0\}} in U {\displaystyle U} ) is a subset of K {\displaystyle K} (such an f {\displaystyle f} is almost everywhere defined in K {\displaystyle K} ). The set L p ( K ) {\displaystyle L^{p}(K)} is a closed vector subspace L p ( U ) {\displaystyle L^{p}(U)} and is thus a Banach space and when p = 2 , {\displaystyle p=2,} even a Hilbert space.77 Let L c p ( U ) {\displaystyle L_{c}^{p}(U)} be the union of all L p ( K ) {\displaystyle L^{p}(K)} as K ⊆ U {\displaystyle K\subseteq U} ranges over all compact subsets of U . {\displaystyle U.} The set L c p ( U ) {\displaystyle L_{c}^{p}(U)} is a vector subspace of L p ( U ) {\displaystyle L^{p}(U)} whose elements are the (equivalence classes of) compactly supported L p {\displaystyle L^{p}} functions defined on U {\displaystyle U} (or almost everywhere on U {\displaystyle U} ). Endow L c p ( U ) {\displaystyle L_{c}^{p}(U)} with the final topology (direct limit topology) induced by the inclusion maps L p ( K ) → L c p ( U ) {\displaystyle L^{p}(K)\to L_{c}^{p}(U)} as K ⊆ U {\displaystyle K\subseteq U} ranges over all compact subsets of U . {\displaystyle U.} This topology is called the canonical LF topology and it is equal to the final topology induced by any countable set of inclusion maps L p ( K n ) → L c p ( U ) {\displaystyle L^{p}(K_{n})\to L_{c}^{p}(U)} ( n = 1 , 2 , … {\displaystyle n=1,2,\ldots } ) where K 1 ⊆ K 2 ⊆ ⋯ {\displaystyle K_{1}\subseteq K_{2}\subseteq \cdots } are any compact sets with union equal to U . {\displaystyle U.} 78 This topology makes L c p ( U ) {\displaystyle L_{c}^{p}(U)} into an LB-space (and thus also an LF-space) with a topology that is strictly finer than the norm (subspace) topology that L p ( U ) {\displaystyle L^{p}(U)} induces on it.
The inclusion map In : C c ∞ ( U ) → C c 0 ( U ) {\displaystyle \operatorname {In} :C_{c}^{\infty }(U)\to C_{c}^{0}(U)} is a continuous injection whose image is dense in its codomain, so the transpose t In : ( C c 0 ( U ) ) b ′ → D ′ ( U ) = ( C c ∞ ( U ) ) b ′ {\displaystyle {}^{t}\operatorname {In} :\left(C_{c}^{0}(U)\right)_{b}^{\prime }\to {\mathcal {D}}^{\prime }(U)=\left(C_{c}^{\infty }(U)\right)_{b}^{\prime }} is also a continuous injection.
Note that the continuous dual space ( C c 0 ( U ) ) b ′ {\displaystyle \left(C_{c}^{0}(U)\right)_{b}^{\prime }} can be identified as the space of Radon measures, where there is a one-to-one correspondence between the continuous linear functionals T ∈ ( C c 0 ( U ) ) b ′ {\displaystyle T\in \left(C_{c}^{0}(U)\right)_{b}^{\prime }} and integral with respect to a Radon measure; that is,
Through the injection t In : ( C c 0 ( U ) ) b ′ → D ′ ( U ) , {\displaystyle {}^{t}\operatorname {In} :\left(C_{c}^{0}(U)\right)_{b}^{\prime }\to {\mathcal {D}}^{\prime }(U),} every Radon measure becomes a distribution on U. If f {\displaystyle f} is a locally integrable function on U then the distribution ϕ ↦ ∫ U f ( x ) ϕ ( x ) d x {\displaystyle \phi \mapsto \textstyle \int _{U}f(x)\phi (x)\,dx} is a Radon measure; so Radon measures form a large and important space of distributions.
The following is the theorem of the structure of distributions of Radon measures, which shows that every Radon measure can be written as a sum of derivatives of locally L ∞ {\displaystyle L^{\infty }} functions in U :
Theorem.79—Suppose T ∈ D ′ ( U ) {\displaystyle T\in {\mathcal {D}}'(U)} is a Radon measure, where U ⊆ R n , {\displaystyle U\subseteq \mathbb {R} ^{n},} let V ⊆ U {\displaystyle V\subseteq U} be a neighborhood of the support of T , {\displaystyle T,} and let I = { p ∈ N n : | p | ≤ n } . {\displaystyle I=\{p\in \mathbb {N} ^{n}:|p|\leq n\}.} There exists a family f = ( f p ) p ∈ I {\displaystyle f=(f_{p})_{p\in I}} of locally L ∞ {\displaystyle L^{\infty }} functions on U such that supp f p ⊆ V {\displaystyle \operatorname {supp} f_{p}\subseteq V} for every p ∈ I , {\displaystyle p\in I,} and T = ∑ p ∈ I ∂ p f p . {\displaystyle T=\sum _{p\in I}\partial ^{p}f_{p}.} Furthermore, T {\displaystyle T} is also equal to a finite sum of derivatives of continuous functions on U , {\displaystyle U,} where each derivative has order ≤ 2 n . {\displaystyle \leq 2n.}
Positive Radon measures
A linear function T on a space of functions is called positive if whenever a function f {\displaystyle f} that belongs to the domain of T is non-negative (meaning that f {\displaystyle f} is real-valued and f ≥ 0 {\displaystyle f\geq 0} ) then T ( f ) ≥ 0. {\displaystyle T(f)\geq 0.} One may show that every positive linear functional on C c 0 ( U ) {\displaystyle C_{c}^{0}(U)} is necessarily continuous (that is, necessarily a Radon measure).80 Lebesgue measure is an example of a positive Radon measure.
One particularly important class of Radon measures are those that are induced locally integrable functions. The function f : U → R {\displaystyle f:U\to \mathbb {R} } is called locally integrable if it is Lebesgue integrable over every compact subset K of U.81 This is a large class of functions which includes all continuous functions and all Lp space L p {\displaystyle L^{p}} functions. The topology on D ( U ) {\displaystyle {\mathcal {D}}(U)} is defined in such a fashion that any locally integrable function f {\displaystyle f} yields a continuous linear functional on D ( U ) {\displaystyle {\mathcal {D}}(U)} – that is, an element of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} – denoted here by T f {\displaystyle T_{f}} , whose value on the test function ϕ {\displaystyle \phi } is given by the Lebesgue integral: ⟨ T f , ϕ ⟩ = ∫ U f ϕ d x . {\displaystyle \langle T_{f},\phi \rangle =\int _{U}f\phi \,dx.}
Conventionally, one abuses notation by identifying T f {\displaystyle T_{f}} with f , {\displaystyle f,} provided no confusion can arise, and thus the pairing between T f {\displaystyle T_{f}} and ϕ {\displaystyle \phi } is often written ⟨ f , ϕ ⟩ = ⟨ T f , ϕ ⟩ . {\displaystyle \langle f,\phi \rangle =\langle T_{f},\phi \rangle .}
If f {\displaystyle f} and g are two locally integrable functions, then the associated distributions T f {\displaystyle T_{f}} and Tg are equal to the same element of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} if and only if f {\displaystyle f} and g are equal almost everywhere (see, for instance, Hörmander (1983, Theorem 1.2.5)). In a similar manner, every Radon measure μ {\displaystyle \mu } on U defines an element of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} whose value on the test function ϕ {\displaystyle \phi } is ∫ ϕ d μ . {\displaystyle \textstyle \int \phi \,d\mu .} As above, it is conventional to abuse notation and write the pairing between a Radon measure μ {\displaystyle \mu } and a test function ϕ {\displaystyle \phi } as ⟨ μ , ϕ ⟩ . {\displaystyle \langle \mu ,\phi \rangle .} Conversely, as shown in a theorem by Schwartz (similar to the Riesz representation theorem), every distribution which is non-negative on non-negative functions is of this form for some (positive) Radon measure.
Test functions as distributions
The test functions are themselves locally integrable, and so define distributions. The space of test functions C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} is sequentially dense in D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} with respect to the strong topology on D ′ ( U ) . {\displaystyle {\mathcal {D}}^{\prime }(U).} 82 This means that for any T ∈ D ′ ( U ) , {\displaystyle T\in {\mathcal {D}}^{\prime }(U),} there is a sequence of test functions, ( ϕ i ) i = 1 ∞ , {\displaystyle (\phi _{i})_{i=1}^{\infty },} that converges to T ∈ D ′ ( U ) {\displaystyle T\in {\mathcal {D}}^{\prime }(U)} (in its strong dual topology) when considered as a sequence of distributions. Or equivalently, ⟨ ϕ i , ψ ⟩ → ⟨ T , ψ ⟩ for all ψ ∈ D ( U ) . {\displaystyle \langle \phi _{i},\psi \rangle \to \langle T,\psi \rangle \qquad {\text{ for all }}\psi \in {\mathcal {D}}(U).}
Furthermore, C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} is also sequentially dense in the strong dual space of C ∞ ( U ) . {\displaystyle C^{\infty }(U).} 83
The inclusion map In : C c ∞ ( U ) → C ∞ ( U ) {\displaystyle \operatorname {In} :C_{c}^{\infty }(U)\to C^{\infty }(U)} is a continuous injection whose image is dense in its codomain, so the transpose t In : ( C ∞ ( U ) ) b ′ → D ′ ( U ) = ( C c ∞ ( U ) ) b ′ {\displaystyle {}^{t}\operatorname {In} :\left(C^{\infty }(U)\right)_{b}^{\prime }\to {\mathcal {D}}^{\prime }(U)=\left(C_{c}^{\infty }(U)\right)_{b}^{\prime }} is also a continuous injection. Thus the image of the transpose, denoted by E ′ ( U ) , {\displaystyle {\mathcal {E}}^{\prime }(U),} forms a space of distributions when it is endowed with the strong dual topology of ( C ∞ ( U ) ) b ′ {\displaystyle \left(C^{\infty }(U)\right)_{b}^{\prime }} (transferred to it via the transpose map t In : ( C ∞ ( U ) ) b ′ → E ′ ( U ) , {\displaystyle {}^{t}\operatorname {In} :\left(C^{\infty }(U)\right)_{b}^{\prime }\to {\mathcal {E}}^{\prime }(U),} so the topology of E ′ ( U ) {\displaystyle {\mathcal {E}}^{\prime }(U)} is finer than the subspace topology that this set inherits from D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} ).84
The elements of E ′ ( U ) = ( C ∞ ( U ) ) b ′ {\displaystyle {\mathcal {E}}^{\prime }(U)=\left(C^{\infty }(U)\right)_{b}^{\prime }} can be identified as the space of distributions with compact support.85 Explicitly, if T is a distribution on U then the following are equivalent,
Compactly supported distributions define continuous linear functionals on the space C ∞ ( U ) {\displaystyle C^{\infty }(U)} ; recall that the topology on C ∞ ( U ) {\displaystyle C^{\infty }(U)} is defined such that a sequence of test functions ϕ k {\displaystyle \phi _{k}} converges to 0 if and only if all derivatives of ϕ k {\displaystyle \phi _{k}} converge uniformly to 0 on every compact subset of U. Conversely, it can be shown that every continuous linear functional on this space defines a distribution of compact support. Thus compactly supported distributions can be identified with those distributions that can be extended from C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} to C ∞ ( U ) . {\displaystyle C^{\infty }(U).}
Let k ∈ N . {\displaystyle k\in \mathbb {N} .} The inclusion map In : C c ∞ ( U ) → C c k ( U ) {\displaystyle \operatorname {In} :C_{c}^{\infty }(U)\to C_{c}^{k}(U)} is a continuous injection whose image is dense in its codomain, so the transpose t In : ( C c k ( U ) ) b ′ → D ′ ( U ) = ( C c ∞ ( U ) ) b ′ {\displaystyle {}^{t}\operatorname {In} :\left(C_{c}^{k}(U)\right)_{b}^{\prime }\to {\mathcal {D}}^{\prime }(U)=\left(C_{c}^{\infty }(U)\right)_{b}^{\prime }} is also a continuous injection. Consequently, the image of t In , {\displaystyle {}^{t}\operatorname {In} ,} denoted by D ′ k ( U ) , {\displaystyle {\mathcal {D}}'^{k}(U),} forms a space of distributions when it is endowed with the strong dual topology of ( C c k ( U ) ) b ′ {\displaystyle \left(C_{c}^{k}(U)\right)_{b}^{\prime }} (transferred to it via the transpose map t In : ( C ∞ ( U ) ) b ′ → D ′ k ( U ) , {\displaystyle {}^{t}\operatorname {In} :\left(C^{\infty }(U)\right)_{b}^{\prime }\to {\mathcal {D}}'^{k}(U),} so D ′ m ( U ) {\displaystyle {\mathcal {D}}'^{m}(U)} 's topology is finer than the subspace topology that this set inherits from D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} ). The elements of D ′ k ( U ) {\displaystyle {\mathcal {D}}'^{k}(U)} are the distributions of order ≤ k . {\displaystyle \,\leq k.} 87 The distributions of order ≤ 0 , {\displaystyle \,\leq 0,} which are also called distributions of order 0 , {\displaystyle 0,} are exactly the distributions that are Radon measures (described above).
For 0 ≠ k ∈ N , {\displaystyle 0\neq k\in \mathbb {N} ,} a distribution of order k {\displaystyle k} is a distribution of order ≤ k {\displaystyle \,\leq k} that is not a distribution of order ≤ k − 1 {\displaystyle \,\leq k-1} 88
A distribution is said to be of finite order if there is some integer k such that it is a distribution of order ≤ k , {\displaystyle \,\leq k,} and the set of distributions of finite order is denoted by D ′ F ( U ) . {\displaystyle {\mathcal {D}}'^{F}(U).} Note that if k ≤ 1 {\displaystyle k\leq 1} then D ′ k ( U ) ⊆ D ′ l ( U ) {\displaystyle {\mathcal {D}}'^{k}(U)\subseteq {\mathcal {D}}'^{l}(U)} so that D ′ F ( U ) {\displaystyle {\mathcal {D}}'^{F}(U)} is a vector subspace of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} and furthermore, if and only if D ′ F ( U ) = D ′ ( U ) . {\displaystyle {\mathcal {D}}'^{F}(U)={\mathcal {D}}^{\prime }(U).} 89
Structure of distributions of finite order
Every distribution with compact support in U is a distribution of finite order.90 Indeed, every distribution in U is locally a distribution of finite order, in the following sense:91 If V is an open and relatively compact subset of U and if ρ V U {\displaystyle \rho _{VU}} is the restriction mapping from U to V, then the image of D ′ ( U ) {\displaystyle {\mathcal {D}}^{\prime }(U)} under ρ V U {\displaystyle \rho _{VU}} is contained in D ′ F ( V ) . {\displaystyle {\mathcal {D}}'^{F}(V).}
The following is the theorem of the structure of distributions of finite order, which shows that every distribution of finite order can be written as a sum of derivatives of Radon measures:
Theorem92—Suppose T ∈ D ′ ( U ) {\displaystyle T\in {\mathcal {D}}^{\prime }(U)} has finite order and I = { p ∈ N n : | p | ≤ k } . {\displaystyle I=\{p\in \mathbb {N} ^{n}:|p|\leq k\}.} Given any open subset V of U containing the support of T, there is a family of Radon measures in U, ( μ p ) p ∈ I , {\displaystyle (\mu _{p})_{p\in I},} such that for very p ∈ I , supp ( μ p ) ⊆ V {\displaystyle p\in I,\operatorname {supp} (\mu _{p})\subseteq V} and T = ∑ | p | ≤ k ∂ p μ p . {\displaystyle T=\sum _{|p|\leq k}\partial ^{p}\mu _{p}.}
Example. (Distributions of infinite order) Let U := ( 0 , ∞ ) {\displaystyle U:=(0,\infty )} and for every test function f , {\displaystyle f,} let S f := ∑ m = 1 ∞ ( ∂ m f ) ( 1 m ) . {\displaystyle Sf:=\sum _{m=1}^{\infty }(\partial ^{m}f)\left({\frac {1}{m}}\right).}
Then S is a distribution of infinite order on U. Moreover, S can not be extended to a distribution on R {\displaystyle \mathbb {R} } ; that is, there exists no distribution T on R {\displaystyle \mathbb {R} } such that the restriction of T to U is equal to T.93
For tempered distributions on semisimple groups, see Tempered representation.
Defined below are the tempered distributions, which form a subspace of D ′ ( R n ) , {\displaystyle {\mathcal {D}}^{\prime }(\mathbb {R} ^{n}),} the space of distributions on R n . {\displaystyle \mathbb {R} ^{n}.} This is a proper subspace: while every tempered distribution is a distribution and an element of D ′ ( R n ) , {\displaystyle {\mathcal {D}}^{\prime }(\mathbb {R} ^{n}),} the converse is not true. Tempered distributions are useful if one studies the Fourier transform since all tempered distributions have a Fourier transform, which is not true for an arbitrary distribution in D ′ ( R n ) . {\displaystyle {\mathcal {D}}^{\prime }(\mathbb {R} ^{n}).}
Schwartz space
The Schwartz space, S ( R n ) , {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n}),} is the space of all smooth functions that are rapidly decreasing at infinity along with all partial derivatives. Thus ϕ : R n → R {\displaystyle \phi :\mathbb {R} ^{n}\to \mathbb {R} } is in the Schwartz space provided that any derivative of ϕ , {\displaystyle \phi ,} multiplied with any power of | x | , {\displaystyle |x|,} converges to 0 as | x | → ∞ . {\displaystyle |x|\to \infty .} These functions form a complete TVS with a suitably defined family of seminorms. More precisely, for any multi-indices α {\displaystyle \alpha } and β {\displaystyle \beta } define: p α , β ( ϕ ) = sup x ∈ R n | x α ∂ β ϕ ( x ) | . {\displaystyle p_{\alpha ,\beta }(\phi )~=~\sup _{x\in \mathbb {R} ^{n}}\left|x^{\alpha }\partial ^{\beta }\phi (x)\right|.}
Then ϕ {\displaystyle \phi } is in the Schwartz space if all the values satisfy: p α , β ( ϕ ) < ∞ . {\displaystyle p_{\alpha ,\beta }(\phi )<\infty .}
The family of seminorms p α , β {\displaystyle p_{\alpha ,\beta }} defines a locally convex topology on the Schwartz space. For n = 1 , {\displaystyle n=1,} the seminorms are, in fact, norms on the Schwartz space. One can also use the following family of seminorms to define the topology:94 | f | m , k = sup | p | ≤ m ( sup x ∈ R n { ( 1 + | x | ) k | ( ∂ α f ) ( x ) | } ) , k , m ∈ N . {\displaystyle |f|_{m,k}=\sup _{|p|\leq m}\left(\sup _{x\in \mathbb {R} ^{n}}\left\{(1+|x|)^{k}\left|(\partial ^{\alpha }f)(x)\right|\right\}\right),\qquad k,m\in \mathbb {N} .}
Otherwise, one can define a norm on S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} via ‖ ϕ ‖ k = max | α | + | β | ≤ k sup x ∈ R n | x α ∂ β ϕ ( x ) | , k ≥ 1. {\displaystyle \|\phi \|_{k}~=~\max _{|\alpha |+|\beta |\leq k}\sup _{x\in \mathbb {R} ^{n}}\left|x^{\alpha }\partial ^{\beta }\phi (x)\right|,\qquad k\geq 1.}
The Schwartz space is a Fréchet space (i.e. a complete metrizable locally convex space). Because the Fourier transform changes ∂ α {\displaystyle \partial ^{\alpha }} into multiplication by x α {\displaystyle x^{\alpha }} and vice versa, this symmetry implies that the Fourier transform of a Schwartz function is also a Schwartz function.
A sequence { f i } {\displaystyle \{f_{i}\}} in S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} converges to 0 in S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} if and only if the functions ( 1 + | x | ) k ( ∂ p f i ) ( x ) {\displaystyle (1+|x|)^{k}(\partial ^{p}f_{i})(x)} converge to 0 uniformly in the whole of R n , {\displaystyle \mathbb {R} ^{n},} which implies that such a sequence must converge to zero in C ∞ ( R n ) . {\displaystyle C^{\infty }(\mathbb {R} ^{n}).} 95
D ( R n ) {\displaystyle {\mathcal {D}}(\mathbb {R} ^{n})} is dense in S ( R n ) . {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n}).} The subset of all analytic Schwartz functions is dense in S ( R n ) {\displaystyle {\mathcal {S}}(\mathbb {R} ^{n})} as well.96
The Schwartz space is nuclear and the tensor product of two maps induces a canonical surjective TVS-isomorphisms S ( R m ) ⊗ ^ S ( R n ) → S ( R m + n ) , {\displaystyle {\mathcal {S}}(\mathbb {R} ^{m})\ {\widehat {\otimes }}\ {\mathcal {S}}(\mathbb {R} ^{n})\to {\mathcal {S}}(\mathbb {R} ^{m+n}),} where ⊗ ^ {\displaystyle {\widehat {\otimes }}} represents the completion of the injective tensor product (which in this case is the identical to the completion of the projective tensor product).97
Tempered distributions
The inclusion map In : D ( R n ) → S ( R n ) {\displaystyle \operatorname {In} :{\mathcal {D}}(\mathbb {R} ^{n})\to {\mathcal {S}}(\mathbb {R} ^{n})} is a continuous injection whose image is dense in its codomain, so the transpose t In : ( S ( R n ) ) b ′ → D ′ ( R n ) {\displaystyle {}^{t}\operatorname {In} :({\mathcal {S}}(\mathbb {R} ^{n}))'_{b}\to {\mathcal {D}}^{\prime }(\mathbb {R} ^{n})} is also a continuous injection. Thus, the image of the transpose map, denoted by S ′ ( R n ) , {\displaystyle {\mathcal {S}}^{\prime }(\mathbb {R} ^{n}),} forms a space of distributions when it is endowed with the strong dual topology of ( S ( R n ) ) b ′ {\displaystyle ({\mathcal {S}}(\mathbb {R} ^{n}))'_{b}} (transferred to it via the transpose map t In : ( S ( R n ) ) b ′ → D ′ ( R n ) , {\displaystyle {}^{t}\operatorname {In} :({\mathcal {S}}(\mathbb {R} ^{n}))'_{b}\to {\mathcal {D}}^{\prime }(\mathbb {R} ^{n}),} so the topology of S ′ ( R n ) {\displaystyle {\mathcal {S}}^{\prime }(\mathbb {R} ^{n})} is finer than the subspace topology that this set inherits from D ′ ( R n ) {\displaystyle {\mathcal {D}}^{\prime }(\mathbb {R} ^{n})} ).
The space S ′ ( R n ) {\displaystyle {\mathcal {S}}^{\prime }(\mathbb {R} ^{n})} is called the space of tempered distributions. It is the continuous dual of the Schwartz space. Equivalently, a distribution T is a tempered distribution if and only if ( for all α , β ∈ N n : lim m → ∞ p α , β ( ϕ m ) = 0 ) ⟹ lim m → ∞ T ( ϕ m ) = 0. {\displaystyle \left({\text{ for all }}\alpha ,\beta \in \mathbb {N} ^{n}:\lim _{m\to \infty }p_{\alpha ,\beta }(\phi _{m})=0\right)\Longrightarrow \lim _{m\to \infty }T(\phi _{m})=0.}
The derivative of a tempered distribution is again a tempered distribution. Tempered distributions generalize the bounded (or slow-growing) locally integrable functions; all distributions with compact support and all square-integrable functions are tempered distributions. More generally, all functions that are products of polynomials with elements of Lp space L p ( R n ) {\displaystyle L^{p}(\mathbb {R} ^{n})} for p ≥ 1 {\displaystyle p\geq 1} are tempered distributions.
The tempered distributions can also be characterized as slowly growing, meaning that each derivative of T grows at most as fast as some polynomial. This characterization is dual to the rapidly falling behaviour of the derivatives of a function in the Schwartz space, where each derivative of ϕ {\displaystyle \phi } decays faster than every inverse power of | x | . {\displaystyle |x|.} An example of a rapidly falling function is | x | n exp ( − λ | x | β ) {\displaystyle |x|^{n}\exp(-\lambda |x|^{\beta })} for any positive n , λ , β . {\displaystyle n,\lambda ,\beta .}
Fourier transform
To study the Fourier transform, it is best to consider complex-valued test functions and complex-linear distributions. The ordinary continuous Fourier transform F : S ( R n ) → S ( R n ) {\displaystyle F:{\mathcal {S}}(\mathbb {R} ^{n})\to {\mathcal {S}}(\mathbb {R} ^{n})} is a TVS-automorphism of the Schwartz space, and the Fourier transform is defined to be its transpose t F : S ′ ( R n ) → S ′ ( R n ) , {\displaystyle {}^{t}F:{\mathcal {S}}^{\prime }(\mathbb {R} ^{n})\to {\mathcal {S}}^{\prime }(\mathbb {R} ^{n}),} which (abusing notation) will again be denoted by F. So the Fourier transform of the tempered distribution T is defined by ( F T ) ( ψ ) = T ( F ψ ) {\displaystyle (FT)(\psi )=T(F\psi )} for every Schwartz function ψ . {\displaystyle \psi .} F T {\displaystyle FT} is thus again a tempered distribution. The Fourier transform is a TVS isomorphism from the space of tempered distributions onto itself. This operation is compatible with differentiation in the sense that F d T d x = i x F T {\displaystyle F{\dfrac {dT}{dx}}=ixFT} and also with convolution: if T is a tempered distribution and ψ {\displaystyle \psi } is a slowly increasing smooth function on R n , {\displaystyle \mathbb {R} ^{n},} ψ T {\displaystyle \psi T} is again a tempered distribution and F ( ψ T ) = F ψ ∗ F T {\displaystyle F(\psi T)=F\psi *FT} is the convolution of F T {\displaystyle FT} and F ψ {\displaystyle F\psi } . In particular, the Fourier transform of the constant function equal to 1 is the δ {\displaystyle \delta } distribution.
Expressing tempered distributions as sums of derivatives
If T ∈ S ′ ( R n ) {\displaystyle T\in {\mathcal {S}}^{\prime }(\mathbb {R} ^{n})} is a tempered distribution, then there exists a constant C > 0 , {\displaystyle C>0,} and positive integers M and N such that for all Schwartz functions ϕ ∈ S ( R n ) {\displaystyle \phi \in {\mathcal {S}}(\mathbb {R} ^{n})} ⟨ T , ϕ ⟩ ≤ C ∑ | α | ≤ N , | β | ≤ M sup x ∈ R n | x α ∂ β ϕ ( x ) | = C ∑ | α | ≤ N , | β | ≤ M p α , β ( ϕ ) . {\displaystyle \langle T,\phi \rangle \leq C\sum \nolimits _{|\alpha |\leq N,|\beta |\leq M}\sup _{x\in \mathbb {R} ^{n}}\left|x^{\alpha }\partial ^{\beta }\phi (x)\right|=C\sum \nolimits _{|\alpha |\leq N,|\beta |\leq M}p_{\alpha ,\beta }(\phi ).}
This estimate along with some techniques from functional analysis can be used to show that there is a continuous slowly increasing function F and a multi-index α {\displaystyle \alpha } such that T = ∂ α F . {\displaystyle T=\partial ^{\alpha }F.}
Restriction of distributions to compact sets
If T ∈ D ′ ( R n ) , {\displaystyle T\in {\mathcal {D}}^{\prime }(\mathbb {R} ^{n}),} then for any compact set K ⊆ R n , {\displaystyle K\subseteq \mathbb {R} ^{n},} there exists a continuous function F compactly supported in R n {\displaystyle \mathbb {R} ^{n}} (possibly on a larger set than K itself) and a multi-index α {\displaystyle \alpha } such that T = ∂ α F {\displaystyle T=\partial ^{\alpha }F} on C c ∞ ( K ) . {\displaystyle C_{c}^{\infty }(K).}
Let U ⊆ R m {\displaystyle U\subseteq \mathbb {R} ^{m}} and V ⊆ R n {\displaystyle V\subseteq \mathbb {R} ^{n}} be open sets. Assume all vector spaces to be over the field F , {\displaystyle \mathbb {F} ,} where F = R {\displaystyle \mathbb {F} =\mathbb {R} } or C . {\displaystyle \mathbb {C} .} For f ∈ D ( U × V ) {\displaystyle f\in {\mathcal {D}}(U\times V)} define for every u ∈ U {\displaystyle u\in U} and every v ∈ V {\displaystyle v\in V} the following functions: f u : V → F and f v : U → F y ↦ f ( u , y ) x ↦ f ( x , v ) {\displaystyle {\begin{alignedat}{9}f_{u}:\,&V&&\to \,&&\mathbb {F} &&\quad {\text{ and }}\quad &&f^{v}:\,&&U&&\to \,&&\mathbb {F} \\&y&&\mapsto \,&&f(u,y)&&&&&&x&&\mapsto \,&&f(x,v)\\\end{alignedat}}}
Given S ∈ D ′ ( U ) {\displaystyle S\in {\mathcal {D}}^{\prime }(U)} and T ∈ D ′ ( V ) , {\displaystyle T\in {\mathcal {D}}^{\prime }(V),} define the following functions: ⟨ S , f ∙ ⟩ : V → F and ⟨ T , f ∙ ⟩ : U → F v ↦ ⟨ S , f v ⟩ u ↦ ⟨ T , f u ⟩ {\displaystyle {\begin{alignedat}{9}\langle S,f^{\bullet }\rangle :\,&V&&\to \,&&\mathbb {F} &&\quad {\text{ and }}\quad &&\langle T,f_{\bullet }\rangle :\,&&U&&\to \,&&\mathbb {F} \\&v&&\mapsto \,&&\langle S,f^{v}\rangle &&&&&&u&&\mapsto \,&&\langle T,f_{u}\rangle \\\end{alignedat}}} where ⟨ T , f ∙ ⟩ ∈ D ( U ) {\displaystyle \langle T,f_{\bullet }\rangle \in {\mathcal {D}}(U)} and ⟨ S , f ∙ ⟩ ∈ D ( V ) . {\displaystyle \langle S,f^{\bullet }\rangle \in {\mathcal {D}}(V).} These definitions associate every S ∈ D ′ ( U ) {\displaystyle S\in {\mathcal {D}}'(U)} and T ∈ D ′ ( V ) {\displaystyle T\in {\mathcal {D}}'(V)} with the (respective) continuous linear map: D ( U × V ) → D ( V ) and D ( U × V ) → D ( U ) f ↦ ⟨ S , f ∙ ⟩ f ↦ ⟨ T , f ∙ ⟩ {\displaystyle {\begin{alignedat}{9}\,&{\mathcal {D}}(U\times V)&&\to \,&&{\mathcal {D}}(V)&&\quad {\text{ and }}\quad &&\,&&{\mathcal {D}}(U\times V)&&\to \,&&{\mathcal {D}}(U)\\&f&&\mapsto \,&&\langle S,f^{\bullet }\rangle &&&&&&f&&\mapsto \,&&\langle T,f_{\bullet }\rangle \\\end{alignedat}}}
Moreover, if either S {\displaystyle S} (resp. T {\displaystyle T} ) has compact support then it also induces a continuous linear map of C ∞ ( U × V ) → C ∞ ( V ) {\displaystyle C^{\infty }(U\times V)\to C^{\infty }(V)} (resp. C ∞ ( U × V ) → C ∞ ( U ) {\displaystyle C^{\infty }(U\times V)\to C^{\infty }(U)} ).98
Fubini's theorem for distributions99—Let S ∈ D ′ ( U ) {\displaystyle S\in {\mathcal {D}}'(U)} and T ∈ D ′ ( V ) . {\displaystyle T\in {\mathcal {D}}'(V).} If f ∈ D ( U × V ) {\displaystyle f\in {\mathcal {D}}(U\times V)} then ⟨ S , ⟨ T , f ∙ ⟩ ⟩ = ⟨ T , ⟨ S , f ∙ ⟩ ⟩ . {\displaystyle \langle S,\langle T,f_{\bullet }\rangle \rangle =\langle T,\langle S,f^{\bullet }\rangle \rangle .}
The tensor product of S ∈ D ′ ( U ) {\displaystyle S\in {\mathcal {D}}'(U)} and T ∈ D ′ ( V ) , {\displaystyle T\in {\mathcal {D}}'(V),} denoted by S ⊗ T {\displaystyle S\otimes T} or T ⊗ S , {\displaystyle T\otimes S,} is the distribution in U × V {\displaystyle U\times V} defined by:100 ( S ⊗ T ) ( f ) := ⟨ S , ⟨ T , f ∙ ⟩ ⟩ = ⟨ T , ⟨ S , f ∙ ⟩ ⟩ . {\displaystyle (S\otimes T)(f):=\langle S,\langle T,f_{\bullet }\rangle \rangle =\langle T,\langle S,f^{\bullet }\rangle \rangle .}
The tensor product defines a bilinear map D ′ ( U ) × D ′ ( V ) → D ′ ( U × V ) ( S , T ) ↦ S ⊗ T {\displaystyle {\begin{alignedat}{4}\,&{\mathcal {D}}^{\prime }(U)\times {\mathcal {D}}^{\prime }(V)&&\to \,&&{\mathcal {D}}^{\prime }(U\times V)\\&~~~~~~~~(S,T)&&\mapsto \,&&S\otimes T\\\end{alignedat}}} the span of the range of this map is a dense subspace of its codomain. Furthermore, supp ( S ⊗ T ) = supp ( S ) × supp ( T ) . {\displaystyle \operatorname {supp} (S\otimes T)=\operatorname {supp} (S)\times \operatorname {supp} (T).} 101 Moreover ( S , T ) ↦ S ⊗ T {\displaystyle (S,T)\mapsto S\otimes T} induces continuous bilinear maps: E ′ ( U ) × E ′ ( V ) → E ′ ( U × V ) S ′ ( R m ) × S ′ ( R n ) → S ′ ( R m + n ) {\displaystyle {\begin{alignedat}{8}&{\mathcal {E}}^{\prime }(U)&&\times {\mathcal {E}}^{\prime }(V)&&\to {\mathcal {E}}^{\prime }(U\times V)\\&{\mathcal {S}}^{\prime }(\mathbb {R} ^{m})&&\times {\mathcal {S}}^{\prime }(\mathbb {R} ^{n})&&\to {\mathcal {S}}^{\prime }(\mathbb {R} ^{m+n})\\\end{alignedat}}} where E ′ {\displaystyle {\mathcal {E}}'} denotes the space of distributions with compact support and S {\displaystyle {\mathcal {S}}} is the Schwartz space of rapidly decreasing functions.102
Schwartz kernel theorem103—Each of the canonical maps below (defined in the natural way) are TVS isomorphisms: S ′ ( R m + n ) ≅ S ′ ( R m ) ⊗ ^ S ′ ( R n ) ≅ L b ( S ( R m ) ; S ′ ( R n ) ) E ′ ( U × V ) ≅ E ′ ( U ) ⊗ ^ E ′ ( V ) ≅ L b ( C ∞ ( U ) ; E ′ ( V ) ) D ′ ( U × V ) ≅ D ′ ( U ) ⊗ ^ D ′ ( V ) ≅ L b ( D ( U ) ; D ′ ( V ) ) {\displaystyle {\begin{alignedat}{8}&{\mathcal {S}}^{\prime }(\mathbb {R} ^{m+n})~&&~\cong ~&&~{\mathcal {S}}^{\prime }(\mathbb {R} ^{m})\ &&{\widehat {\otimes }}\ {\mathcal {S}}^{\prime }(\mathbb {R} ^{n})~&&~\cong ~&&~L_{b}({\mathcal {S}}(\mathbb {R} ^{m});&&\;{\mathcal {S}}^{\prime }(\mathbb {R} ^{n}))\\&{\mathcal {E}}^{\prime }(U\times V)~&&~\cong ~&&~{\mathcal {E}}^{\prime }(U)\ &&{\widehat {\otimes }}\ {\mathcal {E}}^{\prime }(V)~&&~\cong ~&&~L_{b}(C^{\infty }(U);&&\;{\mathcal {E}}^{\prime }(V))\\&{\mathcal {D}}^{\prime }(U\times V)~&&~\cong ~&&~{\mathcal {D}}^{\prime }(U)\ &&{\widehat {\otimes }}\ {\mathcal {D}}^{\prime }(V)~&&~\cong ~&&~L_{b}({\mathcal {D}}(U);&&\;{\mathcal {D}}^{\prime }(V))\\\end{alignedat}}} Here ⊗ ^ {\displaystyle {\widehat {\otimes }}} represents the completion of the injective tensor product (which in this case is identical to the completion of the projective tensor product, since these spaces are nuclear) and L b ( X ; Y ) {\displaystyle L_{b}(X;Y)} has the topology of uniform convergence on bounded subsets.
This result does not hold for Hilbert spaces such as L 2 {\displaystyle L^{2}} and its dual space.104 Why does such a result hold for the space of distributions and test functions but not for other "nice" spaces like the Hilbert space L 2 {\displaystyle L^{2}} ? This question led Alexander Grothendieck to discover nuclear spaces, nuclear maps, and the injective tensor product. He ultimately showed that it is precisely because D ( U ) {\displaystyle {\mathcal {D}}(U)} is a nuclear space that the Schwartz kernel theorem holds. Like Hilbert spaces, nuclear spaces may be thought as of generalizations of finite dimensional Euclidean space.
The success of the theory led to investigation of the idea of hyperfunction, in which spaces of holomorphic functions are used as test functions. A refined theory has been developed, in particular Mikio Sato's algebraic analysis, using sheaf theory and several complex variables. This extends the range of symbolic methods that can be made into rigorous mathematics, for example Feynman integrals.
The Schwartz space consists of smooth rapidly decreasing test functions, where "rapidly decreasing" means that the function decreases faster than any polynomial increases as points in its domain move away from the origin. /wiki/Schwartz_space ↩
Except for the trivial (i.e. identically 0 {\displaystyle 0} ) map, which of course is always analytic. ↩
Trèves 2006, pp. 222–223. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
See for example Grubb 2009, p. 14. - Grubb, G. (2009), Distributions and Operators, Springer ↩
Note that i {\displaystyle i} being an integer implies i ≠ ∞ . {\displaystyle i\neq \infty .} This is sometimes expressed as 0 ≤ i < k + 1. {\displaystyle 0\leq i↩
The image of the compact set K {\displaystyle K} under a continuous R {\displaystyle \mathbb {R} } -valued map (for example, x ↦ | ∂ p f ( x ) | {\displaystyle x\mapsto \left|\partial ^{p}f(x)\right|} for x ∈ U {\displaystyle x\in U} ) is itself a compact, and thus bounded, subset of R . {\displaystyle \mathbb {R} .} If K ≠ ∅ {\displaystyle K\neq \varnothing } then this implies that each of the functions defined above is R {\displaystyle \mathbb {R} } -valued (that is, none of the supremums above are ever equal to ∞ {\displaystyle \infty } ). /wiki/Compact_set ↩
Trèves 2006, pp. 85–89. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 142–149. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 356–358. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Rudin 1991, pp. 149–181. - Rudin, Walter (1991). Functional Analysis. International Series in Pure and Applied Mathematics. Vol. 8 (Second ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 978-0-07-054236-5. OCLC 21163277. https://archive.org/details/functionalanalys00rudi ↩
Trèves 2006, pp. 131–134. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 245–247. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 247–252. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
If we take K {\displaystyle \mathbb {K} } to be the set of all compact subsets of U then we can use the universal property of direct limits to conclude that the inclusion In K U : C k ( K ) → C c k ( U ) {\displaystyle \operatorname {In} _{K}^{U}:C^{k}(K)\to C_{c}^{k}(U)} is a continuous and even that they are topological embedding for every compact subset K ⊆ U . {\displaystyle K\subseteq U.} If however, we take K {\displaystyle \mathbb {K} } to be the set of closures of some countable increasing sequence of relatively compact open subsets of U having all of the properties mentioned earlier in this in this article then we immediately deduce that C c k ( U ) {\displaystyle C_{c}^{k}(U)} is a Hausdorff locally convex strict LF-space (and even a strict LB-space when k ≠ ∞ {\displaystyle k\neq \infty } ). All of these facts can also be proved directly without using direct systems (although with more work). /wiki/LF-space ↩
Trèves 2006, pp. 126–134. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 136–148. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
For any TVS X (metrizable or otherwise), the notion of completeness depends entirely on a certain so-called "canonical uniformity" that is defined using only the subtraction operation (see the article Complete topological vector space for more details). In this way, the notion of a complete TVS does not require the existence of any metric. However, if the TVS X is metrizable and if d {\displaystyle d} is any translation-invariant metric on X that defines its topology, then X is complete as a TVS (i.e. it is a complete uniform space under its canonical uniformity) if and only if ( X , d ) {\displaystyle (X,d)} is a complete metric space. So if a TVS X happens to have a topology that can be defined by such a metric d then d may be used to deduce the completeness of X but the existence of such a metric is not necessary for defining completeness and it is even possible to deduce that a metrizable TVS is complete without ever even considering a metric (e.g. since the Cartesian product of any collection of complete TVSs is again a complete TVS, we can immediately deduce that the TVS R N , {\displaystyle \mathbb {R} ^{\mathbb {N} },} which happens to be metrizable, is a complete TVS; note that there was no need to consider any metric on R N {\displaystyle \mathbb {R} ^{\mathbb {N} }} ). /wiki/Topological_vector_space ↩
One reason for giving C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} the canonical LF topology is because it is with this topology that C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} and its continuous dual space both become nuclear spaces, which have many nice properties and which may be viewed as a generalization of finite-dimensional spaces (for comparison, normed spaces are another generalization of finite-dimensional spaces that have many "nice" properties). In more detail, there are two classes of topological vector spaces (TVSs) that are particularly similar to finite-dimensional Euclidean spaces: the Banach spaces (especially Hilbert spaces) and the nuclear Montel spaces. Montel spaces are a class of TVSs in which every closed and bounded subset is compact (this generalizes the Heine–Borel theorem), which is a property that no infinite-dimensional Banach space can have; that is, no infinite-dimensional TVS can be both a Banach space and a Montel space. Also, no infinite-dimensional TVS can be both a Banach space and a nuclear space. All finite dimensional Euclidean spaces are nuclear Montel Hilbert spaces but once one enters infinite-dimensional space then these two classes separate. Nuclear spaces in particular have many of the "nice" properties of finite-dimensional TVSs (e.g. the Schwartz kernel theorem) that infinite-dimensional Banach spaces lack (for more details, see the properties, sufficient conditions, and characterizations given in the article Nuclear space). It is in this sense that nuclear spaces are an "alternative generalization" of finite-dimensional spaces. Also, as a general rule, in practice most "naturally occurring" TVSs are usually either Banach spaces or nuclear space. Typically, most TVSs that are associated with smoothness (i.e. infinite differentiability, such as C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} and C ∞ ( U ) {\displaystyle C^{\infty }(U)} ) end up being nuclear TVSs while TVSs associated with finite continuous differentiability (such as C k ( K ) {\displaystyle C^{k}(K)} with K compact and k ≠ ∞ {\displaystyle k\neq \infty } ) often end up being non-nuclear spaces, such as Banach spaces. /wiki/Topological_vector_space ↩
Rudin 1991, pp. 149–155. - Rudin, Walter (1991). Functional Analysis. International Series in Pure and Applied Mathematics. Vol. 8 (Second ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 978-0-07-054236-5. OCLC 21163277. https://archive.org/details/functionalanalys00rudi ↩
Narici & Beckenstein 2011, pp. 446–447. - Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834. https://search.worldcat.org/oclc/144216834 ↩
Trèves 2006, p. 423. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Even though the topology of C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} is not metrizable, a linear functional on C c ∞ ( U ) {\displaystyle C_{c}^{\infty }(U)} is continuous if and only if it is sequentially continuous. ↩
A null sequence is a sequence that converges to the origin. ↩
A sequence x ∙ = ( x i ) i = 1 ∞ {\displaystyle x_{\bullet }=\left(x_{i}\right)_{i=1}^{\infty }} is said to be Mackey convergent to 0 in X , {\displaystyle X,} if there exists a divergent sequence r ∙ = ( r i ) i = 1 ∞ → ∞ {\displaystyle r_{\bullet }=\left(r_{i}\right)_{i=1}^{\infty }\to \infty } of positive real number such that ( r i x i ) i = 1 ∞ {\displaystyle \left(r_{i}x_{i}\right)_{i=1}^{\infty }} is a bounded set in X . {\displaystyle X.} ↩
If P {\displaystyle {\mathcal {P}}} is also a directed set under the usual function comparison then we can take the finite collection to consist of a single element. /wiki/Directed_set ↩
In functional analysis, the strong dual topology is often the "standard" or "default" topology placed on the continuous dual space X ′ , {\displaystyle X',} where if X is a normed space then this strong dual topology is the same as the usual norm-induced topology on X ′ . {\displaystyle X'.} /wiki/Functional_analysis ↩
See for example Schaefer & Wolff 1999, p. 173. - Schaefer, Helmut H.; Wolff, Manfred P. (1999). Topological Vector Spaces. GTM. Vol. 8 (Second ed.). New York, NY: Springer New York Imprint Springer. ISBN 978-1-4612-7155-0. OCLC 840278135. https://search.worldcat.org/oclc/840278135 ↩
Technically, the topology must be coarser than the strong dual topology and also simultaneously be finer that the weak* topology. /wiki/Weak*_topology ↩
Gabriyelyan, Saak "Topological properties of Strict LF-spaces and strong duals of Montel Strict LF-spaces" (2017) https://arxiv.org/pdf/1702.07867.pdf ↩
Gabriyelyan, S.S. Kakol J., and·Leiderman, A. "The strong Pitkeev property for topological groups and topological vector spaces" https://prometeo2013058.blogs.upv.es/files/2014/02/sPp-MM.pdf ↩
Trèves 2006, pp. 195–201. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Narici & Beckenstein 2011, p. 435. - Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834. https://search.worldcat.org/oclc/144216834 ↩
Trèves 2006, pp. 526–534. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, p. 357. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
"Topological vector space". Encyclopedia of Mathematics. Retrieved September 6, 2020. It is a Montel space, hence paracompact, and so normal. https://encyclopediaofmath.org/wiki/Topological_vector_space ↩
T. Shirai, Sur les Topologies des Espaces de L. Schwartz, Proc. Japan Acad. 35 (1959), 31-36. ↩
According to Gel'fand & Shilov 1966–1968, v. 1, §1.2 - Gel'fand, I.M.; Shilov, G.E. (1966–1968), Generalized functions, vol. 1–5, Academic Press ↩
Trèves 2006, pp. 351–359. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Recall that a linear map is bounded if and only if it maps null sequences to bounded sequences. ↩
Narici & Beckenstein 2011, pp. 441–457. - Narici, Lawrence; Beckenstein, Edward (2011). Topological Vector Spaces. Pure and applied mathematics (Second ed.). Boca Raton, FL: CRC Press. ISBN 978-1584888666. OCLC 144216834. https://search.worldcat.org/oclc/144216834 ↩
Trèves 2006, pp. 150–160. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 300–304. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Strichartz 1994, §2.3; Trèves 2006. - Strichartz, R. (1994), A Guide to Distribution Theory and Fourier Transforms, CRC Press, ISBN 0-8493-8273-4 ↩
Trèves 2006, pp. 131–135. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 240–245. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 240–252. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 262–264. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, p. 218. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
For more information on such class of functions, see the entry on locally integrable functions. /wiki/Locally_integrable ↩
Trèves 2006, pp. 255–257. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 258–264. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Rudin 1991, pp. 177–181. - Rudin, Walter (1991). Functional Analysis. International Series in Pure and Applied Mathematics. Vol. 8 (Second ed.). New York, NY: McGraw-Hill Science/Engineering/Math. ISBN 978-0-07-054236-5. OCLC 21163277. https://archive.org/details/functionalanalys00rudi ↩
Trèves 2006, pp. 92–94. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 160. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, p. 531. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 416–419. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩
Trèves 2006, pp. 509–510. - Trèves, François (2006) [1967]. Topological Vector Spaces, Distributions and Kernels. Mineola, N.Y.: Dover Publications. ISBN 978-0-486-45352-1. OCLC 853623322. https://search.worldcat.org/oclc/853623322 ↩