nLab Banach space

Banach spaces

Banach spaces


A Banach space \mathcal{B} is both a vector space (over a normed field such as \mathbb{R}) and a complete metric space, in a compatible way. Hence a complete normed vector space.

A source of simple Banach spaces comes from considering a Cartesian space n\mathbb{R}^n (or K nK^n where KK is the normed field) with the norm:

(x 1,,x n) p i=1 n|x i| pp {\|(x_1,\ldots,x_n)\|_p} \coloneqq \root p {\sum_{i = 1}^n {|x_i|^p}}

where 1p1 \leq p \leq \infty (this doesn’t strictly make sense for p=p = \infty, but taking the limit as pp \to \infty and reading =lim n n\mathbb{R}^\infty = \underset{\longrightarrow}{\lim}_n \mathbb{R}^n as the direct limit (as opposed to the inverse limit) we arrive at the formula (x 1,,x n) max i|x i|{\|(x_1,\ldots,x_n)\|_\infty} \coloneqq \max_i {|x_i|}).

However, the theory of these spaces is not much more complicated than that of finite-dimensional vector spaces because they all have the same underlying topology. When we look at infinite-dimensional examples, however, things become trickier. Common examples are Lebesgue spaces, Hilbert spaces, and sequence spaces.

In the literature, one most often sees Banach spaces over the field \mathbb{R} of real numbers; Banach spaces over the field \mathbb{C} of complex numbers are not much different, since they are also over \mathbb{R}. But people do study them over p-adic numbers too. Unless otherwise stated, we assume \mathbb{R} below.


Let VV be a vector space over the field of real numbers. (One can generalise the choice of field somewhat.) A pseudonorm (or seminorm) on VV is a function

:V {\| - \|}\colon V \to \mathbb{R}

such that:

  1. 00 {\|0\|} \geq 0 ;
  2. rv=|r|v {\|r v\|} = {|r|} {\|v\|} (for rr a scalar and vv a vector);
  3. v+wv+w {\|v + w\|} \leq {\|v\|} + {\|w\|} .

It follows from the above that v0{\|v\|} \geq 0; in particular, 0=0{\|0\|} = 0. A norm is a pseudonorm that satisfies a converse to this: v=0v = 0 if v=0{\|v\|} = 0.

A norm on VV is complete if, given any infinite sequence (v 1,v 2,)(v_1, v_2, \ldots) such that

(1)lim m,n i=m m+nv i=0, \lim_{m,n\to\infty} {\left\| \sum_{i=m}^{m+n} v_i \right\|} = 0 ,

there exists a (necessarily unique) sum SS such that

(2)lim nS i=1 nv i=0; \lim_{n\to\infty} {\left\| S - \sum_{i=1}^n v_i \right\|} = 0 ;

we write

S= i=1 v i S = \sum_{i=1}^\infty v_i

(with the right-hand side undefined if no such sum exists).

Then a Banach space is simply a vector space equipped with a complete norm. As in the real line, we have in a Banach space that

i=1 v i i=1 v i, {\left\| \sum_{i=1}^\infty v_i \right\|} \leq \sum_{i=1}^\infty {\|v_i\|} ,

with the left-hand side guaranteed to exist if the right-hand side exists as a finite real number (but the left-hand side may exist even if the right-hand side diverges, the usual distinction between absolute convergence and conditional convergence).

If we do not insist on the space being complete, we call it a normed (vector) space. If we have a topological vector space such that the topology comes from a norm, but we do not make an actual choice of such a norm, then we talk of a normable space.

Banach spaces as metric spaces

The three axioms for a pseudonorm are very similar to the three axioms for a pseudometric.

Indeed, in any pseudonormed vector space, let the distance d(v,w)d(v,w) be

d(v,w)=wv. d(v,w) = {\|w - v\|} .

Then dd is a pseudometric, which is translation-invariant in that

d(v+x,w+x)=d(v,w) d(v+x,w+x) = d(v,w)

always holds. Conversely, given any translation-invariant pseudometric dd on a vector space VV, let v{\|v\|} be

v=d(0,v). {\|v\|} = d(0,v) .

Then {\|-\|} satisfies the axioms (1–3) for a pseudonorm, except that it may satisfy (2) only for r=0,±1r = 0, \pm 1. (In other words, it is only a G-pseudonorm.) It will actually be a pseudonorm iff the pseudometric satisfies a homogeneity rule:

d(rv,rw)=|r|d(v,w). d(r v,r w) = {|r|} d(v,w) .

Thus pseudonorms correspond precisely to homogeneous translation-invariant pseudometrics.

Similarly, norms correspond to homogenous translation-invariant metrics and complete norms correspond to complete homogeneous translation-invariant metrics. Indeed, (1) says that the sequence of partial sums is a Cauchy sequence, while (2) says that the sequence of partial sums converges to SS.

Thus a Banach space may equivalently be defined as a vector space equipped with a complete homogeneous translation-invariant metric. Actually, one usually sees a sort of hybrid approach: a Banach space is a normed vector space whose corresponding metric is complete.

Maps between Banach spaces

If VV and WW are pseudonormed vector spaces, then the norm of a linear function f:VWf\colon V \to W may be defined in either of these equivalent ways:

  • f=sup{fv|v1} {\|f\|} = \sup \{ {\|f v\|} \;|\; {\|v\|} \leq 1 \} ;
  • f=inf{r|v,fvrv} {\|f\|} = \inf \{ r \;|\; \forall{v},\; {\|f v\|} \leq r {\|v\|} \} .

(Some other forms are sometimes seen, but these may break down in degenerate cases.)

For finite-dimensional spaces, any linear map has a well-defined finite norm. In general, the following are equivalent:

In this case, we say that ff is bounded. If f:VWf\colon V \to W is not assumed to be linear, then the above conditions are no longer equivalent.

The bounded linear maps from VV to WW themselves form a pseudonormed vector space (V,W)\mathcal{B}(V,W). This will be a Banach space if (and, except for degenerate cases of VV, only if) WW is a Banach space. In this way, the category BanBan of Banach spaces is a closed category with \mathbb{R} as the unit.

The clever reader will note that we have not yet defined Ban\mathbf{Ban} as a category! (surprisingly in the nLab) There are many (nonequivalent) ways to do so.

In functional analysis, the usual notion of ‘isomorphism’ for Banach spaces is a bounded bijective linear map f:VWf\colon V \to W such that the inverse function f 1:WVf^{-1}\colon W \to V (which is necessarily linear) is also bounded. In this case one can accept all bounded linear maps between Banach spaces as morphisms. Analysts sometimes refer to this as the “isomorphic category”.

Another natural notion of isomorphism is a surjective linear isometry. In this case, we take a morphism to be a short linear map, or linear contraction: a linear map ff such that f1{\|f\|} \leq 1. This category, which is what category theorists generally refer to as Ban\mathbf{Ban}, is sometimes referred to as the “isometric category” by analysts. Note that this makes the ‘underlying set’ (in the sense of Ban\mathbf{Ban} as a concrete category like any closed category) of a Banach space its (closed) unit ball

Hom Ban(,V){v|v1} Hom_Ban(\mathbb{R},V) \cong \{ v \;|\; {\|v\|} \leq 1 \}

rather than the set of all vectors in VV (the underlying set of VV as a vector space).

Yemon Choi: This is really here to remind myself how to make query boxes. But while I’m at it, is it really OK to refer to the “unit ball functor” as “taking the underlying set”? I notice that on the discussion about internal homs at internal hom it is claimed that “Every closed category is a concrete category (represented by II), and the underlying set of the internal hom is the external hom” which seems to require “underlying set” to be interpreted in this looser sense.

Toby: Sure, but the point of putting ‘underlying set’ in scare quotes is precisely to point out that the category-theoretic underlying set is not what one would normally expect.

Mark Meckes: I’ve expanded this section in part to be consistent with analysts’ terminology. I’ve made some assumptions about category theorists’ conventions which might not be correct. (If I find time I might write about other categories of Banach spaces that analysts think about.)

Toby: Looks good to me!

From a category-theorist's perspective, the isomorphic category is really the full image of the inclusion functor from BanBan to TVSTVS (the category of topological vector spaces), which may be denoted Ban TVSBan_{TVS}. If you're working in Ban TVSBan_{TVS}, then you only care about the topological linear structure of your space (although you do also care that it can be derived from some metric); if you're working in BanBan, then you care about all of the structure on the space.

As a unit ball with extra structure

Since the forgetful functor Hom Ban(,):BanSetHom_Ban(\mathbb{R},-)\colon Ban \to Set, taking each Banach space to its closed unit ball, is faithful, it's natural to consider a Banach space as this ball with extra structure. We can always do this by abstract nonsense: define a Banach space to be a set BB equipped with a bijection to the closed unit ball of some complete normed vector space.

However, we can also do this in a more explicit way, constructing a Banach space as a structured set. The key is that, while a vector space supports any finite linear combinations (and a complete TVS supports some infinite ones), the unit ball of a Banach space supports linear combinations (finite or infinite) whose coefficients' absolute values sum to at most 11. (As a special case, this includes convex-linear combinations, where all coefficients are positive and sum to 11, so the unit ball is a convex space.)

So we define a Banach space to be set BB (the underlying set, or closed unit ball to be unambiguous, whose elements are called short vectors to distinguish them from the more general elements of the underlying vector space of BB) equipped with, for each multiset AA of scalars whose absolute values sum to at most 11, an AA-indexed operation on BB, such that these operations are compatible as linear combinations, together with a function \|{-}\| from BB to the unit interval [0,1][0,1] satisfying homogeneity, definiteness, normalizability (defined below), and the infinitary triangle inequality that makes BB into a complete metric space.

To be more explicit:

  • Given a set II (thought of as an index set), a short II-sequence of scalars is a scalar-valued function aa on II (whose values are written a ia_i for ii in II) such that i:I|a i|1\sum_{i\colon I} {|{a_i}|} \leq 1 (with, in constructive mathematics, the classically redundant assumption that the left-hand side of this inequality exists). In other words, aa is in the sequence space l 1(I)l^1(I).

  • Similarly, an II-sequence of short vectors is a BB-valued function xx on II (whose values are written x ix_i for ii in II), with no requirements.

  • For each set II, each short II-sequence aa of scalars, and each II-sequence xx of short vectors, we require a short vector i:Ia ix i\sum_{i\colon I} a_i x_i. (Special cases include the empty sum 00, x=1x-x = -1 x, and 12x+12y\frac 1 2 x + \frac 1 2 y, but not x+yx + y.)

  • For each short vector xx, we require 1x=x1 x = x and 0x=00 x = 0 (although the latter is redundant with a homogenous definite norm).

  • For each set II, each II-indexed family JJ of sets (so J iJ_i is a set for each ii in II), each short II-sequence aa of scalars, each II-indexed family bb of short J iJ_i-sequences of scalars (so b i,jb_{i,j} is a scalar for each ii in II and jj in J iJ_i, and j:J i|b i,j|1\sum_{j\colon J_i} {|{b_{i,j}}|} \leq 1 for each ii in II), and each II-indexed family xx of J iJ_i-sequences of short vectors (so x i,jx_{i,j} is an element of BB for each ii in II and jj in J iJ_i), we require i:Ia i j:J ib i,jx i,j= i:I j:J ia ib i,jx i,j\sum_{i\colon I} a_i \sum_{j\colon J_i} b_{i,j} x_{i,j} = \sum_{i\colon I} \sum_{j\colon J_i} a_i b_{i,j} x_{i,j} (which makes sense since i:I j:J i|a i||b i,j|1\sum_{i\colon I} \sum_{j\colon J_i} {|{a_i}|}\, {|{b_{i,j}}|} \leq 1). (This is what it means for the linear-combination operations to be compatible as linear combinations.)

  • For each short vector xx, we require a norm x\|{x}\|, a scalar ranging from 00 to 11.

  • For each short scalar aa (so |a|1|{a}| \leq 1) and each short vector xx, we require ax=|a|x{\|{a x}\|} = {|{a}|}\, {\|{x}\|}. (This is homogeneity.)

  • For each set II, each short II-sequence aa of scalars, and each II-sequence xx of short vectors, we require i:Ia ix i i:I|a i|x i{\|{\sum_{i\colon I} a_i x_i}\|} \leq \sum_{i\colon I} {|{a_i}|}\, {\|{x_i}\|}. (This is the infinitary triangle inequality.)

  • For each short vector xx, if x=0{\|{x}\|} = 0, then we require x=0x = 0 (this is definiteness).

  • For each short vector xx, if x>0{\|{x}\|} \gt 0, then we require a short vector x^\hat{x} such that x=xx^x = {\|{x}\|}\, \hat{x} (this is what I'm calling normalizability). (By homogeneity, x^=1{\|{\hat{x}}\|} = 1.)

  • For each short vector xx and short vector yy, although we cannot form xyx - y, we can define the distance d(x,y)d(x,y) to be 212x12y2 {\|{\frac 1 2 x - \frac 1 2 y}\|}. (Then for each scalar a2a \geq 2, d(x,y)=a1ax1ayd(x,y) = a {\|{\frac 1 a x - \frac 1 a y}\|}, using homogeneity.)

  • If (x 1,x 2,x 3,)(x_1, x_2, x_3, \ldots) is a Cauchy sequence (or net in constructive mathematics), we require that the sequence converges. (We automatically get lim n i=1 na ix i= i=1 a ix i \lim _ { n \to \infty } \sum _ { i = 1 } ^ n a _ i x _ i = \sum _ { i = 1 } ^ \infty a _ i x _ i from what has come before, but not every Cauchy sequence takes this form.)

We can then define a vector to be a formal scalar multiple of a short vector. Specifically, a vector is an equivalence class of pairs (a,x)(a,x), where aa is a scalar and xx is a short vector, where (a,x)(a,x) is equivalent to (b,y)(b,y) iff acx=bcy\frac a c x = \frac b c y for some nonzero scalar cc such that |ac|1{|{\frac a c}|} \leq 1 and |bc|1{|{\frac b c}|} \leq 1, which without loss of generality can be c=max(|a|,|b|,1)c = \max ({|{a}|},{|{b}|},1). Similarly, we can define [(a,x)]+[(b,y)]=[(c,acx+bcy)][(a,x)] + [(b,y)] = [(c,\frac a c x + \frac b c y)], where now c=max(12|a|,12|b|,12)c = \max (\frac 1 2 {|{a}|},\frac 1 2 {|{b}|},\frac 1 2) or any other nonzero scalar such that |ac|+|bc|1{|{\frac a c}|} + {|{\frac b c}|} \leq 1. (More generally, we can define finitary linear combinations of vectors, although not always infinitary ones, even if the coefficients are a short sequence; that’s an advantage of the unit ball.) And we can define b[(a,x)]b [(a,x)] even more easily as [(ab,x)][(a b,x)], and [(a,x)]|a|x{\|{[(a,x)]}\|} \coloneqq {|{a}|}\, {\|{x}\|}. This makes the set of vectors into a Banach space in the usual sense, and its closed unit ball is equivalent to BB, consisting of all vectors of the form [(1,x)][(1,x)].


Many examples of Banach spaces are parametrised by an exponent 1p1 \leq p \leq \infty. (Sometimes one can also try 0p<10 \leq p \lt 1, but these generally don't give Banach spaces.)

  • The Cartesian space n\mathbb{R}^n is a Banach space with

    (x 1,,x n) p= i|x i| pp. {\|(x_1,\ldots,x_n)\|_p} = \root p {\sum_i {|x_i|^p}} .

    (We can allow p=p = \infty by taking a limit; the result is that x =max i|x i|{\|x\|_\infty} = \max_i {|x_i|}.) Every finite-dimensional Banach space is isomorphic to this for some nn and pp; in fact, once you fix nn, the value of pp is irrelevant up to isomorphism.

  • The sequence space l pl^p is the set of infinite sequences (x 1,x 2,)(x_1,x_2,\ldots) of real numbers such that

    (x 1,x 2,) p= i|x i| pp {\|(x_1,x_2,\ldots)\|_p} = \root p {\sum_i {|x_i|^p}}

    exists as a finite real number. (The only question is whether the sum converges. Again p=p = \infty is a limit, with the result that x =sup i|x i|{\|x\|_\infty} = \sup_i {|x_i|}.) Then l pl^p is a Banach space with that norm. These are all versions of \mathbb{R}^\infty, but they are no longer isomorphic for different values of pp. (See isomorphism classes of Banach spaces.)

  • More generally, let AA be any set and let l p(A)l^p(A) be the set of functions ff from AA to \mathbb{R} such that

    f p= x:A|f(x)| pp {\|f\|_p} = \root p {\sum_{x: A} {|f(x)|^p}}

    exists as a finite real number. (Again, f =sup x:A|f(x)|{\|f\|_\infty} = \sup_{x\colon A} {|f(x)|}.) Then l p(A)l^p(A) is a Banach space. (This example includes the previous examples, for AA a countable set.)

  • On any measure space XX, the Lebesgue space p(X)\mathcal{L}^p(X) is the set of measurable almost-everywhere-defined real-valued functions on XX such that

    f p=|f| pp {\|f\|_p} = \root p {\int {|f|^p}}

    exists as a finite real number. (Again, the only question is whether the integral converges. And again p=p = \infty is a limit, with the result that f {\|f\|_\infty} is the essential supremum of |f|{|f|}.) As such, p(X)\mathcal{L}^p(X) is a complete pseudonormed vector space; but we identify functions that are equal almost everywhere to make it into a Banach space. (This example includes the previous examples, for XX a set with counting measure.)

  • Any Hilbert space is Banach space; this includes all of the above examples for p=2p = 2.

Operations on Banach spaces

The category BanBan of Banach spaces is small complete, small cocomplete, and symmetric monoidal closed with respect to its standard internal hom (described at internal hom). Some details follow.

  • The category of Banach spaces admits small products. Given a small family of Banach spaces {X α} αA\{X_\alpha\}_{\alpha \in A}, its product in BanBan is the subspace of the vector-space product

    αAX α\prod_{\alpha \in A} X_\alpha

    consisting of AA-tuples x α\langle x_\alpha \rangle which are uniformly bounded (i.e., there exists CC such that αA:x αC\forall \alpha \in A: {\|x_\alpha\|} \leq C), taking the least such upper bound as the norm of x α\langle x_\alpha \rangle. This norm is called the \infty-norm; in particular, the product of an AA-indexed family of copies of \mathbb{R} or \mathbb{C} is what is normally denoted as l (A)l^{\infty}(A).

  • The category of Banach spaces admits equalizers. Indeed, the equalizer of a pair of maps f,g:XYf, g: X \rightrightarrows Y in BanBan is the kernel of fgf-g under the norm inherited from XX (the kernel is closed since fgf-g is continuous, and is therefore complete). In fact every equalizer is even a section by the Hahn-Banach theorem. Every extremal monomorphism is even already an equalizer (and a section): Let f:XYf\colon X \to Y be an extremal monomorphism, ι:(f)Y\iota\colon \Im(f) \to Y the embedding of Im(f)Im(f) into the codomain of ff and f:XIm(f)f\prime \colon X \to Im(f) ff with restricted codomain. Since ff\prime is an epimorphism, f=ιff=\iota f\prime, and ff extremal, ff\prime is an isomorphism, thus ff is an embedding.

  • The category of Banach spaces admits small coproducts. Given a small family of Banach spaces {X α} αA\{X_\alpha\}_{\alpha \in A}, its coproduct in BanBan is the completion of the vector space coproduct

    αAX α\bigoplus_{\alpha \in A} X_\alpha

    with respect to the norm given by

    sSx s= sSx s, {\left\| \bigoplus_{s \in S} x_s \right\|} = \sum_{s \in S} {\|x_s\|} ,

    where SAS \subseteq A is finite and x s{\|x_s\|} denotes the norm of an element in X sX_s. This norm is called the 11-norm; in particular, the coproduct of an AA-indexed family of copies of \mathbb{R} or \mathbb{C} is what is normally denoted as l 1(A)l^1(A).

  • The category of Banach spaces admits coequalizers. Though one may expect the coequalizer of a pair of maps f,g:XYf,g: X \rightrightarrows Y to be the cokernel of fgf-g under the quotient norm (in which the norm of a coset y+Cy + C is the minimum norm attained by elements of y+Cy + C; here CC is the image (fg)(X)(f-g)(X)), these spaces are not Banach spaces in general, as the image of a map ff need not be closed (indeed, the inclusion i: 1c 0i: \ell_1 \hookrightarrow c_0 has dense image in c 0c_0), and so the quotient space may not be complete. However, the quotient by the closure of (fg)(X)(f-g)(X) suffices.

  • To describe the tensor product X BanYX \otimes_{Ban} Y of two Banach spaces (making BanBan symmetric monoidal closed with respect to its usual internal hom), let F(X×Y)F(X \times Y) be the free vector space generated by the set X×YX \times Y, with norm on a typical element defined by

    1ina i(x iy i)= 1in|a i|x iy i. {\left\| \sum_{1 \leq i \leq n} a_i (x_i \otimes y_i) \right\|} = \sum_{1 \leq i \leq n} {|a_i|} {\|x_i\|} \cdot {\|y_i\|}.

    Let F¯(X×Y)\overline{F}(X \times Y) denote its completion with respect to this norm. Then take the cokernel of F¯(X×Y)\overline{F}(X \times Y) by the closure of the subspace spanned by the obvious bilinear relations. This quotient is X BanYX \otimes_{Ban} Y.

In the literature on Banach spaces, tensor product above is usually called the projective tensor product of Banach spaces; see other tensor product of Banach spaces. The product and coproduct are considered direct sums; see other direct sums of Banach spaces.

To be described:

  • duals (p+q=pqp + q = p q);
  • completion (BanBan is a reflective subcategory of PsNVectPsNVect (pseudo-normed vector spaces)).
  • BanBan as a (somewhat larger) category with duals.

Integration in Banach spaces

This paragraph describes some aspects of integration theory in Banach spaces that are relevant to understand the literature about AQFT. In the given context, elements of a Banach space \mathcal{B} are sometimes called vectors, a function or measure taking values in \mathcal{B} are therefore called vector functions and vector measures. Functions and measures taking values in the field that the Banach space is defined upon as a vector space are called scalar functions and scalar measures.

We will consider two types of integrals:

  • integrals of vector functions with respect to a scalar measure, specifically the Bochner integral,

  • integrals of scalar functions with respect to a vector measure, specifically the spectral integral of a normal operator on a Hilbert space.

Bochner integral

The Bochner integral is a direct generalization of the Lesbegue integral to functions that take values in a Banach space. Whenever you encounter an integral of a function taking values in a Banach space in the AQFT literature, it is safe to assume that it is meant to be a Bochner integral. Two points already explained by Wikipedia are of interest:

  1. A version of the dominated convergence theorem is true for the Bochner integral.
  2. There are theorems that are not valid for the Bochner integral, notably the Radon-Nikodym theorem does not hold in general.

reference: Joseph Diestel: “Sequences and Series in Banach Spaces” (ZMATH entry), chapter IV.

The spectral integral

The integral with respect to the spectral measure of a bounded normal operator on a Hilbert space is an example of a Banach space integral with respect to a vector measure. In this paragraph we present a well known, but somewhat less often cited result, that is of use in some proofs in some approaches to AQFT, it is the version of the dominated convergence theorem for the given setting.

Let A be a bounded normal operator on a Hilbert space and E be it’s spectral measure (the “resolution of identity” in the terms of Dunford and Schwartz). Let σ(A)\sigma(A) be the spectrum of A. For a bounded complex Borel function f we then have

f(A) σ(A)f(λ)E(dλ) f(A) \coloneqq \int_{\sigma(A)} f(\lambda) E(d\lambda)
Theorem (dominated convergence)

If the uniformly bounded sequence {f n}\{f_n\} of complex Borel functions converges at each point of σ(A)\sigma(A) to the function ff, then f n(A)f(A)f_n(A) \to f(A) in the strong operator topology.

See Dunford, Schwartz II, chapter X, corollary 8.


Relation to bornological spaces

Every inductive limit of Banach spaces is a bornological vector space. (Alpay-Salomon 13, prop. 2.3)

Conversely, every bornological vector space is an inductive limit of normed spaces, and of Banach spaces if it is quasi-complete (Schaefer-Wolff 99)

algebraic structuregroupringfieldvector spacealgebra
(submultiplicative) normnormed groupnormed ringnormed fieldnormed vector spacenormed algebra
multiplicative norm (absolute value/valuation)valued field
completenesscomplete normed groupBanach ringcomplete fieldBanach vector spaceBanach algebra


Named after Stefan Banach.

  • Walter Rudin, Functional analysis

  • Dunford, Nelson; Schwartz, Jacob T.: “Linear operators. Part I: General theory.” (ZMATH entry), “Linear operators. Part II: Spectral theory, self adjoint operators in Hilbert space.” (ZMATH entry)

  • Z. Semadeni, Banach spaces of continuous functions, vol. I, Polish scientific publishers. Warszawa 1971

  • Daniel Alpay, Guy Salomon, On algebras which are inductive limits of Banach spaces (arXiv:1302.3372)

  • H. H. Schaefer with M. P. Wolff, Topological vector spaces, Springer 1999

  • Jiří Rosický, Are Banach spaces monadic?, (arXiv:2011.07543)

category: analysis

Last revised on March 11, 2024 at 08:20:21. See the history of this page for a list of all contributions to it.