Spatial tensor products: complete proof supplement through the commutation theorem
Original text by Claude Opus 5.5 (Anthropic), September 2026; public domain (CC0). Scoped selection and prerequisite bindings by GPT-6.1 Sol (OpenAI), Ultra, October 2026; original contributions CC0. The mathematical text of the selected proofs is retained.
Sections 1–11 give the complete construction of spatial tensor products and the tensor commutation theorem. Hilbert spaces and orthogonal families can have arbitrary cardinality. Countable vector series describe one normal functional and impose no separability hypothesis.
The proofs use Hilbert completion, projection and Riesz representation over the real and complex fields, bounded adjoints, continuous functional calculus and positive-functional Cauchy–Schwarz. Their full published providers are the Hilbert-space, continuous-calculus and GNS chapters of Foundations of von Neumann algebras. Section 4 supplies its own bicommutant proof.
For Theorem 10.1(3), use the published Kaplansky chapter, Theorem 7.1, and the published trace-class/predual chapter, Theorem 9.4(a). For Theorem 10.1(4), use the bounded normal-functional support proof in the universal-enveloping chapter, “State and support tools used below” and Lemma 11.1. For the comments following Corollaries 8.3–8.4, the universal-enveloping chapter, Corollary 11.4, proves normality of isomorphisms; the tensor-isomorphism proof itself explicitly tensors the given normal inverse. These four incoming statements are full proofs in the published foundation reader.
Section 11 proves tensor commutation by real orthogonal complements and cyclic corners. General Tomita–Takesaki theory, standard-form modular implementers, weights and semifiniteness are not prerequisites of this proof.
Conventions
Hilbert spaces are complex. Inner products are linear in the first variable. No separability or dimension condition is imposed anywhere, and the zero space is allowed. A sum over an arbitrary index set is the limit of the net of its finite partial sums. Every Hilbert space has an orthonormal basis.
B(H) is the algebra of bounded operators on H. For ξ,η∈H put ωξ,η(x)=⟨xξ,η⟩ and ωξ=ωξ,ξ. For a set S⊆B(H), S′ is its commutant. It is a unital algebra, closed in the weak operator topology, and a ∗-algebra when S∗=S. We call a ∗-algebra M⊆B(H) with M′′=M a von Neumann algebra on H. It contains 1 and is weakly closed. On the zero space, B(0)={0} is a von Neumann algebra.
1. The Hilbert tensor product
Let H⊙K be the algebraic tensor product. The universal property of ⊙, used once in each variable, gives a unique sesquilinear form on H⊙K with
⟨ξ⊗η,ξ′⊗η′⟩=⟨ξ,ξ′⟩⟨η,η′⟩.(1.1)
Lemma 1.1. The form (1.1) is an inner product.
Proof. Let ζ=∑i=1nξi⊗ηi. Choose an orthonormal basis u1,…,ur of the span of the ηi, and write ηi=∑lcilul. Then ζ=∑lξl′⊗ul with ξl′=∑icilξi, and (1.1) gives
⟨ζ,ζ⟩=∑l,m⟨ξl′,ξm′⟩⟨ul,um⟩=∑l∥ξl′∥2≥0.
If this is 0, every ξl′ is 0. The ul are linearly independent, so ζ=0. □
Definition 1.2. The Hilbert tensor productH⊗K is the completion of H⊙K for the inner product (1.1).
Proposition 1.3.
(Continuity and totality.) ∥ξ⊗η∥=∥ξ∥∥η∥ and ∥ξ⊗η−ξ′⊗η′∥≤∥ξ−ξ′∥∥η∥+∥ξ′∥∥η−η′∥. If D⊆H and E⊆K are total, the vectors ξ⊗η with ξ∈D, η∈E are total in H⊗K. If (ei) and (fj) are orthonormal bases, (ei⊗fj) is an orthonormal basis.
(Vector maps.) For η∈K and ξ∈H, the maps Rηξ′=ξ′⊗η and Sξη′=ξ⊗η′ are bounded operators Rη:H→H⊗K and Sξ:K→H⊗K, with ∥Rηξ′∥=∥ξ′∥∥η∥ and ∥Sξη′∥=∥ξ∥∥η′∥. Their adjoints act by Rη∗(ξ′⊗η′)=⟨η′,η⟩ξ′ and Sξ∗(ξ′⊗η′)=⟨ξ′,ξ⟩η′. Rη and Sξ are linear in η and ξ, so Rη∗ and Sξ∗ are conjugate-linear in them.
(Columns.) Let (fj)j∈J be an orthonormal basis of K and put Rj=Rfj. Then Rj∗Rk=δjk1H, and for every ζ∈H⊗Kζ=j∑RjRj∗ζ,∥ζ∥2=j∑∥Rj∗ζ∥2.(1.2)
Conversely, ∑jRjζj converges whenever ∑j∥ζj∥2<∞. Rows Si=Sei, for an orthonormal basis (ei) of H, behave in the same way. If K=0, then J=∅ and H⊗K=0.
(Flip and associativity.) There are unitaries H⊗K→K⊗H, ξ⊗η↦η⊗ξ, and (H⊗K)⊗L→H⊗(K⊗L), (ξ⊗η)⊗θ↦ξ⊗(η⊗θ). Also ξ⊗c↦cξ is a unitary H⊗C→H.
We identify the two triple products through the associativity unitary, and write H⊗K⊗L and ξ⊗η⊗θ. We call (1.2) the column expansion.
Proof. (1) The norm identity is (1.1). The estimate follows from ξ⊗η−ξ′⊗η′=(ξ−ξ′)⊗η+ξ′⊗(η−η′). By bilinearity, the closed span of the given vectors contains ξ⊗η for ξ in the span of D and η in the span of E. By the estimate it then contains every ξ⊗η, hence the dense subspace H⊙K. Orthonormality of (ei⊗fj) follows from (1.1), and totality from what was just shown.
(2) Boundedness is the norm identity. For the adjoint, ⟨ξ′⊗η′,Rηξ′′⟩=⟨ξ′,ξ′′⟩⟨η′,η⟩=⟨⟨η′,η⟩ξ′,ξ′′⟩. The same computation works for Sξ.
(3) ⟨Rkξ,Rjξ′⟩=⟨ξ,ξ′⟩⟨fk,fj⟩ gives Rj∗Rk=δjk1. So the ranges Rj(H) are mutually orthogonal closed subspaces, and RjRj∗ is the projection onto Rj(H). Their closed span contains every ξ⊗fj, hence all of H⊗K by (1). So H⊗K is the orthogonal sum of the Rj(H), which is (1.2). The converse is convergence of orthogonal series.
(4) The maps are defined on the algebraic tensor products by the universal property. They preserve (1.1) on elementary tensors, hence inner products of finite sums. For associativity, (H⊙K)⊙L is dense in (H⊗K)⊗L by (1), since H⊙K is dense in H⊗K. The ranges are dense by (1). Isometries between dense subspaces with dense range extend to unitaries. Finally ⟨ξ⊗c,ξ′⊗c′⟩=ccˉ′⟨ξ,ξ′⟩=⟨cξ,c′ξ′⟩, and the map is onto. □
2. Tensor products of operators and operator matrices
Proposition 2.1.
For a∈B(H) and b∈B(K) there is a unique a⊗b∈B(H⊗K) with (a⊗b)(ξ⊗η)=aξ⊗bη. Moreover ∥a⊗b∥=∥a∥∥b∥, the map (a,b)↦a⊗b is bilinear, (a⊗b)(c⊗d)=ac⊗bd, (a⊗b)∗=a∗⊗b∗, and 1⊗1=1.
(Matrices.) Fix an orthonormal basis (fj)j∈J of K and the columns Rj of Proposition 1.3(3). For X∈B(H⊗K) put Xjk=Rj∗XRk∈B(H). Then Rj∗Xζ=∑kXjkRk∗ζ, a norm-convergent sum, so X is determined by its matrix. Further (a⊗b)jk=⟨bfk,fj⟩a, Rj∗(a⊗1)=aRj∗ and (a⊗1)Rk=Rka.
(Commutation criterion.) X commutes with a⊗1 if and only if every Xjk commutes with a. Hence, for every S⊆B(H),
(S⊗1)′={X∈B(H⊗K):Xjk∈S′for all j,k},S⊗1={s⊗1:s∈S}.(2.1)
(Commuting with 1⊗B(K).) Let K=0. An operator X commutes with every 1⊗b, b∈B(K), if and only if X=x⊗1 for some x∈B(H). Then x=Xjj for every j, and ∥x∥=∥X∥.
(Truncations.) For a finite set F⊆J, let pF be the projection onto the span of {fj:j∈F}, and let ejk∈B(K) be the operator η↦⟨η,fk⟩fj. Then
(1⊗pF)X(1⊗pF)=j,k∈F∑Xjk⊗ejk,(2.2)
its norm is at most ∥X∥, and it converges strongly to X as F increases.
Proof. (1) Two bounded operators that agree on elementary tensors are equal, because elementary tensors are total (Proposition 1.3(1)); this gives uniqueness. For existence, let a⊙1 be the linear map ξ⊗η↦aξ⊗η on H⊙K. On elementary tensors Rj∗(aξ⊗η)=⟨η,fj⟩aξ=aRj∗(ξ⊗η), so Rj∗(a⊙1)ζ=aRj∗ζ for ζ∈H⊙K. By the column expansion (1.2),
∥(a⊙1)ζ∥2=j∑∥aRj∗ζ∥2≤∥a∥2j∑∥Rj∗ζ∥2=∥a∥2∥ζ∥2.
So a⊙1 extends to an operator a⊗1 with ∥a⊗1∥≤∥a∥. Rows give 1⊗b with ∥1⊗b∥≤∥b∥. The two operators commute on elementary tensors, hence everywhere, and a⊗b:=(a⊗1)(1⊗b) has the required values. So ∥a⊗b∥≤∥a∥∥b∥. Conversely ∥a⊗b∥≥∥aξ∥∥bη∥ for unit vectors ξ,η, and the supremum of the right side is ∥a∥∥b∥; if H or K is 0, both sides are 0. The algebraic rules hold on elementary tensors. For the adjoint, ⟨(a⊗b)(ξ⊗η),ξ′⊗η′⟩=⟨aξ,ξ′⟩⟨bη,η′⟩=⟨ξ⊗η,a∗ξ′⊗b∗η′⟩; sesquilinearity and density finish.
(2) By (1.2) and continuity, Xζ=∑kXRkRk∗ζ; apply Rj∗. Next, Rj∗(a⊗b)Rkξ=Rj∗(aξ⊗bfk)=⟨bfk,fj⟩aξ. The last two identities are the case b=1 and its adjoint.
(3) By (2), (X(a⊗1))jk=Rj∗XRka=Xjka and ((a⊗1)X)jk=aXjk. Operators are determined by their matrices.
(4) One direction is clear. For the other, note that 1⊗ejk=RjRk∗, since both send ξ⊗η to ⟨η,fk⟩ξ⊗fj. If X commutes with every RjRk∗, then, using Rk∗Rk=1,
Xjk=Rj∗X(RkRk∗)Rk=Rj∗(RkRk∗)XRk=δjkXkk,Xjj=Rj∗X(RjRk∗)Rk=Rj∗(RjRk∗)XRk=Xkk.
So X has the matrix of x⊗1 with x=Xkk, and X=x⊗1 by (2). By (1), ∥x⊗1∥=∥x∥.
(5) 1⊗pF=∑j∈FRjRj∗, and RjXjkRk∗=Xjk⊗ejk, since both send ξ⊗η to ⟨η,fk⟩Xjkξ⊗fj. This gives (2.2). Put QF=1⊗pF. Then QF→1 strongly by (1.2), and QFXQFζ−Xζ=QFX(QFζ−ζ)+(QF−1)Xζ→0. □
3. Normal functionals and the amplification
The ultraweak topology. Let B(H)∗ be the set of functionals
ρ(x)=n∑⟨xξn,ηn⟩,n∑∥ξn∥2<∞,n∑∥ηn∥2<∞,(3.1)
with countable or finite index sets. A square-summable family of any size has only countably many nonzero members, so larger index sets give nothing new. The series converges absolutely, and ∣ρ(x)∣≤∥x∥(∑∥ξn∥2)1/2(∑∥ηn∥2)1/2. B(H)∗ is a linear space: merge two families to add, and scale one sequence to multiply by a scalar. The ultraweak topology, also called the σ-weak topology, is the weakest topology in which every ρ∈B(H)∗ is continuous. It is finer than the weak operator topology, so every weakly closed set, in particular every commutant, is ultraweakly closed.
A linear map Φ from a subspace of B(H) to B(L) is normal if it is ultraweakly continuous, that is, if ρ∘Φ is ultraweakly continuous for every ρ∈B(L)∗. For a subspace Y⊆B(H), Y∗ denotes its normal functionals. Compositions of normal maps are normal. For fixed A∈B(L,L′) and C∈B(L′,L), the map X↦AXC from B(L) to B(L′) is normal, since ∑n⟨AXCζn,ζn′⟩=∑n⟨X(Cζn),A∗ζn′⟩. For positive maps, normality is often expressed instead through suprema of bounded increasing nets; part (3) of the next proposition gives this order form for the amplification.
Proposition 3.1.
(Normal functionals.) Let Y⊆B(H) be any linear subspace. A linear functional on Y is ultraweakly continuous if and only if it is the restriction of some ρ∈B(H)∗. In particular, every normal functional on Y is bounded.
(Normality of the amplification.) The map a↦a⊗1K, B(H)→B(H⊗K), is a unital ∗-homomorphism. It is isometric if K=0 and zero if K=0. It is normal: for ρ=∑nωζn,ζn′∈B(H⊗K)∗,
ρ(a⊗1)=n∑j∑⟨aRj∗ζn,Rj∗ζn′⟩,n,j∑∥Rj∗ζn∥2=n∑∥ζn∥2,(3.2)
so ρ(⋅⊗1)∈B(H)∗. It is strongly continuous on bounded sets. The same holds for b↦1H⊗b, and for the restriction of either map to any subspace, such as a von Neumann algebra.
(Order form.) Let (aα) be a bounded increasing net of self-adjoint operators on H. It converges strongly to its least upper bound a among self-adjoint operators. The net (aα⊗1) is increasing, converges strongly to a⊗1, and a⊗1 is its least upper bound. If all aα lie in a von Neumann algebra M, then a∈M, and x↦x⊗1 carries the supremum in M to the supremum in every von Neumann algebra that contains M⊗1.
(Normal functionals as vector functionals.) Let M⊆B(H) be a ∗-subalgebra with 1∈M, let φ∈M∗, and let R be an infinite-dimensional Hilbert space. There are ζ,ζ′∈H⊗R with φ(x)=⟨(x⊗1)ζ,ζ′⟩ for x∈M. If φ is positive, one can take ζ′=ζ. Then φ=∑nωξn∣M with ∑n∥ξn∥2=φ(1).
Reference: Part (4) extends [Takesaki I, Lemma IV.5.4], which assumes R separable and gives two vectors. The proof of [Takesaki I, Theorem IV.5.5] needs a single vector at that point; the positive case of (4) supplies it.
Proof. (1) Restrictions are continuous by definition. Conversely, let φ be continuous at 0. There are ρ1,…,ρm∈B(H)∗ and δ>0 with ∣φ(y)∣<1 whenever y∈Y and maxi∣ρi(y)∣<δ. If ρi(y)=0 for all i, the same holds for ty, t>0; so ∣φ(y)∣<1/t for every t, and φ(y)=0. Hence φ vanishes on the kernel of y↦(ρ1(y),…,ρm(y))∈Cm. So φ factors through the image of this map. Extending the factor linearly to Cm gives constants ci with φ=∑iciρi on Y, and ∑iciρi∈B(H)∗. Boundedness follows from the estimate after (3.1).
(2) The algebraic properties and the norm are Proposition 2.1(1). By the column expansion (1.2) and polarization, ⟨(a⊗1)ζ,ζ′⟩=∑j⟨Rj∗(a⊗1)ζ,Rj∗ζ′⟩=∑j⟨aRj∗ζ,Rj∗ζ′⟩, using Rj∗(a⊗1)=aRj∗ from Proposition 2.1(2). This is (3.2); the double family (Rj∗ζn) is square-summable, and so is (Rj∗ζn′). Now let aα→a strongly with ∥aα∥≤C. Then ∥((aα−a)⊗1)ζ∥2=∑j∥(aα−a)Rj∗ζ∥2. Each term tends to 0 and is at most 4C2∥Rj∗ζ∥2. Given ε>0, a finite set of indices carries all of ∑j∥Rj∗ζ∥2 except ε, so the sum tends to 0. Rows handle 1⊗b. Restrictions of continuous maps are continuous.
(3) For ξ∈H, ⟨aαξ,ξ⟩ is increasing and bounded, so it converges. By polarization ⟨aαξ,η⟩ converges for all ξ,η. The limit is a bounded sesquilinear form, so it equals ⟨aξ,η⟩ for a self-adjoint a. Clearly a≥aα for every α. A self-adjoint upper bound b satisfies ⟨bξ,ξ⟩≥limα⟨aαξ,ξ⟩=⟨aξ,ξ⟩, so a is the least upper bound. For T=a−aα≥0, the Cauchy–Schwarz inequality for the positive form ⟨T⋅,⋅⟩ gives
∥Tξ∥4=⟨Tξ,Tξ⟩2≤⟨Tξ,ξ⟩⟨T2ξ,Tξ⟩≤⟨Tξ,ξ⟩∥T∥∥Tξ∥2.
So ∥Tξ∥2≤∥T∥⟨Tξ,ξ⟩→0, and aα→a strongly. For c≥0, ⟨(c⊗1)ζ,ζ⟩=∑j⟨cRj∗ζ,Rj∗ζ⟩≥0; hence (aα⊗1) is increasing. It is bounded and converges strongly to a⊗1 by (2). By the first part its least upper bound is its strong limit a⊗1. A von Neumann algebra is strongly closed, so a∈M, and a is then also the supremum in M. If P⊇M⊗1 is a von Neumann algebra, a⊗1∈P is the least upper bound in all of B(H⊗K), hence in P.
(4) By (1), φ=∑nωξn,ηn∣M with square-summable families indexed by a set N0⊆N. Choose an orthonormal family (gn)n∈N0 in R, and put ζ=∑nξn⊗gn and ζ′=∑nηn⊗gn. By the column expansion (1.2), applied with a basis of R that contains the gn, ⟨(x⊗1)ζ,ζ′⟩=∑n⟨xξn,ηn⟩=φ(x).
Now let φ be positive, and write π(x)=x⊗1R. A positive functional on a unital ∗-algebra is hermitian: a self-adjoint x equals y∗y−z∗z with y=(1+x)/2 and z=(1−x)/2, so φ(x) is real. Hence, for x=x∗, with θ=ζ+ζ′ and θ′=ζ−ζ′,
4φ(x)=2⟨π(x)ζ,ζ′⟩+2⟨π(x)ζ′,ζ⟩=⟨π(x)θ,θ⟩−⟨π(x)θ′,θ′⟩.
So 4φ(y∗y)≤∥π(y)θ∥2 for y∈M. Let L0 be the closure of π(M)θ. The Cauchy–Schwarz inequality for φ gives
∣φ(y∗x)∣2≤φ(x∗x)φ(y∗y)≤161∥π(x)θ∥2∥π(y)θ∥2.
So B(π(x)θ,π(y)θ):=φ(y∗x) is a well-defined positive sesquilinear form on π(M)θ, bounded by 41. It extends to L0, where it is given by a positive operator T0∈B(L0). For a∈M we have B(π(a)u,v)=B(u,π(a∗)v) on π(M)θ, since both sides are φ(y∗ax). So T0 commutes with the restrictions of π(M) to the invariant subspace L0. The projection P0 onto L0 commutes with π(M), because L0 is invariant under the self-adjoint set π(M). Hence T=T0P0 is positive and commutes with π(M), and so does T1/2, a norm limit of polynomials in T. With ξ′′=T1/2θ,
φ(x)=B(π(x)θ,θ)=⟨Tπ(x)θ,θ⟩=⟨π(x)ξ′′,ξ′′⟩.
Expanding ξ′′=∑kξk⊗gk along an orthonormal basis (gk) of R gives φ=∑kωξk∣M and φ(1)=∥ξ′′∥2=∑k∥ξk∥2. □
Remarks. Part (1) is finite-dimensional linear algebra, and the boundedness of a normal functional is a consequence, not a hypothesis. In part (4) any infinite-dimensional R works, while a finite-dimensional one does not in general (Exercise 3.4). The one-vector form for positive φ is used in the proof of Theorem 8.2. Part (2) is not strong continuity on all of B(H): Example 3.2 shows that it fails for unbounded nets when H and K are both infinite-dimensional.
Example 3.2 (the amplification is not strongly continuous). Let H=K=ℓ2(N) and ζ0=∑kk−1δk⊗δk∈H⊗K. For each finite set F⊆H, choose a unit vector uF orthogonal to F, put cF=(∑kk−2∣⟨δk,uF⟩∣2)−1/2, and let xFξ=cF⟨ξ,uF⟩δ1. Then xFξ=0 for ξ∈F, so the net (xF), directed by inclusion, tends to 0 strongly. But (xF⊗1)ζ0=cF∑kk−1⟨δk,uF⟩δ1⊗δk has norm 1. So x↦x⊗1 is strongly continuous only on bounded sets, as in Proposition 3.1(2). Both dimensions matter. If dimK=d<∞, it is strongly continuous everywhere, since ∥(x⊗1)ζ∥2=∑j≤d∥xRj∗ζ∥2 is a finite sum. If dimH<∞, the strong topology on B(H) is the norm topology, and continuity is automatic.
Example 3.3 (the norm need not be attained by a normal functional). For x in a von Neumann algebra, ∥x∥=sup{∣ω(x)∣:ωnormal,∥ω∥≤1}, because the vector functionals ωξ,η with unit vectors ξ,η already give this supremum. The supremum need not be attained. Let x be the diagonal operator with entries 1−1/n, n≥1, on ℓ2(N), so ∥x∥=1, and let ω∈B(ℓ2)∗ with ∥ω∥≤1. For the projection qN onto the span of the first N basis vectors, let aN and bN be the norms of y↦ω(qNyqN) and y↦ω((1−qN)y(1−qN)). Block-diagonal test operators show that aN+bN≤1. Indeed, let y1,y2 have norm at most one, multiplied by scalars of modulus one so that ω(qNy1qN)≥0 and ω((1−qN)y2(1−qN))≥0. The operator qNy1qN+(1−qN)y2(1−qN) has norm at most one, so the sum of these two numbers is at most ∥ω∥≤1; taking suprema over y1 and y2 gives the claim. Since x=qNxqN+(1−qN)x(1−qN) and ∥qNxqN∥=1−1/N, we get ∣ω(x)∣≤(1−1/N)aN+bN≤1−aN/N. So ∣ω(x)∣=1 would force aN=0 and bN=1 for all N. But bN→0: writing ω=∑kωξk,ηk, bN≤(∑k∥(1−qN)ξk∥2)1/2(∑k∥(1−qN)ηk∥2)1/2, which tends to 0 by dominated convergence. Hence no normal functional of norm at most one attains the norm of x.
Exercise 3.4 (the space R in Proposition 3.1(4) must be infinite-dimensional). Let H be infinite-dimensional with an orthonormal sequence (δn), let φ=∑n2−nωδn on B(H), and let R=Cd with d<∞. Show that no ζ,ζ′∈H⊗R satisfy φ(x)=⟨(x⊗1)ζ,ζ′⟩ for all x∈B(H).
Solution. Suppose they do. Expanding along a basis of R gives φ(x)=∑k≤d⟨xξk,ηk⟩ for some vectors ξk,ηk. The linear map v↦(⟨v,ξk⟩)k≤d on the span of δ1,…,δd+1 has a nonzero kernel, so there is a unit vector u in that span with u⊥ξk for all k. Let pu be the projection onto Cu. Then puξk=0, so ∑k⟨puξk,ηk⟩=0. But φ(pu)=∑n2−n∣⟨δn,u⟩∣2>0, since u=0 lies in the span of the δn. This is a contradiction.
4. The bicommutant theorem and the normal extension principle
Lemma 4.1 (amplified density). Let A⊆B(H) be a ∗-subalgebra with 1∈A, and let x∈A′′. For every sequence (ξn) in H with ∑n∥ξn∥2<∞, finite families included, and every ε>0, there is a∈A with
n∑∥(x−a)ξn∥2<ε2.(4.1)
Proof. Let R=ℓ2(N) with standard basis (gn), and ζ=∑nξn⊗gn∈H⊗R. Let E be the closure of {(a⊗1)ζ:a∈A} and P the projection onto E. E is invariant under the self-adjoint set A⊗1: for a∈A we have (a⊗1)P=P(a⊗1)P, and replacing a by a∗ and taking adjoints gives P(a⊗1)=P(a⊗1)P. So P∈(A⊗1)′. By the commutation criterion (2.1), every matrix entry Pjk lies in A′. As x∈A′′, x commutes with every Pjk, and Proposition 2.1(3) shows that x⊗1 commutes with P. Since 1∈A, ζ∈E, so (x⊗1)ζ=P(x⊗1)ζ∈E. Choose a∈A with ∥((x−a)⊗1)ζ∥<ε. By the column expansion (1.2), the square of the left side is ∑n∥(x−a)ξn∥2. □
Theorem 4.2 (bicommutant theorem). For a ∗-subalgebra A⊆B(H) with 1∈A, A′′ is the closure of A in the strong, the weak and the ultraweak topology. A ∗-subalgebra that contains 1 and is closed in one of these topologies is a von Neumann algebra.
Proof. Strong density is Lemma 4.1 for finite families. For ultraweak density, let x∈A′′ and ρ1,…,ρm∈B(H)∗ with ρi=∑nωξni,ηni. Apply Lemma 4.1 to the merged family (ξni)i,n. Then ∣ρi(x−a)∣≤(∑n∥(x−a)ξni∥2)1/2(∑n∥ηni∥2)1/2 is small for all i at once. Conversely, A′′ is weakly closed and the weak topology is coarser than the other two, so each closure of A lies in A′′. The last sentence follows. □
The general form of the theorem, for ∗-subalgebras that need not contain 1, is proved in the lesson The double commutation theorem. The unital case is all we need.
Proposition 4.3 (normal extension principle). Let M be a von Neumann algebra on H, A⊆M a ∗-subalgebra with 1∈A and A′′=M, and Φ,Ψ:M→B(L) normal linear maps.
(i) If Φ=Ψ on A, then Φ=Ψ.
(ii) If P⊆B(L) is a von Neumann algebra and Φ(A)⊆P, then Φ(M)⊆P.
(iii) Let S⊆M be a self-adjoint set with S′′=M, and let Φ,Ψ be ∗-homomorphisms. If Φ(S)⊆P and Φ(1)∈P, then Φ(M)⊆P. If Φ=Ψ on S∪{1}, then Φ=Ψ.
Proof. (i) Let ρ∈B(L)∗. The functional ρ∘(Φ−Ψ) is normal on M. By Proposition 3.1(1) it is the restriction of some σ=∑nωξn,ηn∈B(H)∗, and σ vanishes on A. For x∈M and a∈A,
∣σ(x)∣=∣σ(x−a)∣≤(∑n∥(x−a)ξn∥2)1/2(∑n∥ηn∥2)1/2,
which Lemma 4.1 makes arbitrarily small. So ρ(Φ(x))=ρ(Ψ(x)) for every ρ, in particular for every vector functional, and Φ(x)=Ψ(x).
(ii) For p′∈P′, the map x↦Φ(x)p′−p′Φ(x) is normal and vanishes on A. By (i) it vanishes on M. So Φ(M)⊆P′′=P.
(iii) Let A be the span of 1 and all finite products of elements of S. It is a ∗-subalgebra containing 1 and S, and A′=S′, so A′′=S′′=M. Since Φ is a ∗-homomorphism and P is a unital ∗-algebra, Φ(A)⊆P; in the second case Φ=Ψ on A. Apply (ii) and (i). □
For example, let G be a locally compact group with left regular representation λ. The set S=λ(G) is self-adjoint, because λ(g)∗=λ(g−1), and it contains 1=λ(e). So by (iii), a normal ∗-homomorphism on the group von Neumann algebra λ(G)′′ is determined by its values on the λ(g), and it maps λ(G)′′ into every von Neumann algebra that contains the images of the λ(g).
5. The spatial tensor product
Definition 5.1. Let M⊆B(H) and N⊆B(K) be von Neumann algebras. M⊙N is the linear span of the operators x⊗y with x∈M, y∈N. By Proposition 2.1(1) it is a ∗-subalgebra of B(H⊗K) containing 1. The spatial tensor product is
M⊗ˉN=(M⊙N)′′,(5.1)
the smallest von Neumann algebra that contains every x⊗y. For sets S⊆B(H) and T⊆B(K) we write S⊗1={s⊗1:s∈S} and 1⊗T={1⊗t:t∈T}.
Theorem 5.2.
(Closure.) M⊗ˉN is a von Neumann algebra on H⊗K. It is the closure of M⊙N in the strong, the weak and the ultraweak topology. In particular it is strongly closed.
(Generators.) If S⊆B(H) and T⊆B(K) are self-adjoint sets with S′′=M and T′′=N, then M⊗ˉN=(S⊗1∪1⊗T)′′. In particular M⊗ˉN=(M⊗1∪1⊗N)′′.
(Bounded strong limits.) Let (Xα) be a bounded net in B(H⊗K), let X∈B(H⊗K), and let D⊆H⊗K be a total set with Xαζ→Xζ for every ζ∈D. Then Xα→X strongly. If every Xα lies in M⊗ˉN, so does X. One may take D={ξ⊗η:ξ∈D1,η∈D2} for total sets D1⊆H, D2⊆K.
(Amplification.) For every set S⊆B(H), (S⊗1)′′=S′′⊗1. Hence M⊗1 is a von Neumann algebra, M⊗ˉC1K=M⊗1 and C1H⊗ˉN=1⊗N. The map x↦x⊗1 is a normal unital ∗-homomorphism of M onto M⊗1⊆M⊗ˉN. It is injective and isometric when K=0.
(Matrices over M.) With matrices as in Proposition 2.1(2),
M⊗ˉB(K)={X∈B(H⊗K):Xjk∈Mfor all j,k}=(M′⊗1)′,(5.2)
and B(H)⊗ˉB(K)=B(H⊗K).
Proof. (1) By (5.1), M⊗ˉN is the double commutant of a self-adjoint set, hence a von Neumann algebra. The rest is the bicommutant theorem (Theorem 4.2) with A=M⊙N.
(2) By the commutation criterion (2.1), (S⊗1)′ consists of the operators whose entries lie in S′=S′′′=M′. So (S⊗1)′=(M⊗1)′. Rows give (1⊗T)′=(1⊗N)′. Hence (S⊗1∪1⊗T)′=(M⊗1∪1⊗N)′. The last set equals (M⊙N)′, because M⊗1 and 1⊗N lie in M⊙N and x⊗y=(x⊗1)(1⊗y). Take commutants.
(3) Let C=supα∥Xα∥+∥X∥. Convergence holds on the span of D by linearity. Given ζ and ε>0, pick ζ0 in that span with ∥ζ−ζ0∥<ε. Then ∥(Xα−X)ζ∥≤∥(Xα−X)ζ0∥+Cε, so limsupα∥(Xα−X)ζ∥≤Cε. The second claim follows from (1), since M⊗ˉN is strongly closed. The third follows from the totality of product vectors (Proposition 1.3(1)).
(4) If K=0, all sets involved are {0}. Let K=0. By (2.1), (S⊗1)′ contains 1⊗B(K), whose matrix entries are scalars. So (S⊗1)′′⊆(1⊗B(K))′=B(H)⊗1 by Proposition 2.1(4). Now let x∈B(H). If x∈S′′, then x commutes with the entries of every X∈(S⊗1)′, since they lie in S′; by Proposition 2.1(3), x⊗1 commutes with X. If conversely x⊗1∈(S⊗1)′′, then it commutes with c⊗1 for every c∈S′, so xc=cx and x∈S′′. Thus (S⊗1)′′=S′′⊗1. For S=M this says (M⊗1)′′=M⊗1. Applying Proposition 2.1(4) with H=C gives {1K}′′=B(K)′=C1K; so (2) with T={1} gives M⊗ˉC1=(M⊗1)′′=M⊗1, and symmetrically for N. The remaining claims are the normality of the amplification (Proposition 3.1(2)) and the norm identity of Proposition 2.1(1).
(5) The entries of x⊗y are multiples of x, so M⊙B(K)⊆C:={X:Xjk∈M}. By (2.1), C=(M′⊗1)′, the commutant of a self-adjoint set; so C′′=C and M⊗ˉB(K)⊆C. Conversely, for X∈C the truncations (2.2) lie in M⊙B(K), are bounded by ∥X∥ and converge strongly to X. By (3), X∈M⊗ˉB(K). For M=B(H) we have M′=C1 and (C1⊗1)′=B(H⊗K). □
Part (3) is how membership in a tensor product is usually proved in practice: one exhibits a norm-bounded net in M⊙N that converges on a total set of test vectors. Example 12.2 does this for a discrete group, and the end of Section 12 describes the same argument for a locally compact group. Without the norm bound, convergence on a total set does not give strong convergence (Example 5.5). Part (2) allows generating sets that are not von Neumann algebras themselves. For instance, let G be a locally compact group, m the multiplication representation of L∞(G) on L2(G), and λ the left regular representation. Both m(L∞(G)) and λ(G) are self-adjoint sets, so m(L∞(G))′′⊗ˉλ(G)′′=(m(L∞(G))⊗1∪1⊗λ(G))′′, whether or not m(L∞(G)) is already a von Neumann algebra.
Example 5.3 (diagonal algebras of any size). Let I and J be arbitrary sets, possibly uncountable, and let ℓ∞(I) act on ℓ2(I) by multiplication. It is its own commutant, by the argument given below for I×J, so it is a von Neumann algebra. Counting measure on an uncountable set is not σ-finite, so the σ-finite result in the lesson on decomposable operators and the diagonal algebra does not apply. By Proposition 1.3(1), δi⊗δj↦δ(i,j) is a unitary ℓ2(I)⊗ℓ2(J)→ℓ2(I×J), and under it
ℓ∞(I)⊗ˉℓ∞(J)=ℓ∞(I×J).(5.3)
Indeed, ma⊗mb becomes multiplication by (i,j)↦a(i)b(j). The algebra ℓ∞(I×J) is its own commutant: an operator commuting with every rank-one projection m1{(i,j)} is diagonal, and a bounded diagonal operator is a multiplication. So ℓ∞(I×J) is a von Neumann algebra that contains every ma⊗mb, and ℓ∞(I)⊗ˉℓ∞(J)⊆ℓ∞(I×J). Conversely, for f∈ℓ∞(I×J) and finite F⊆I×J, the operator mf1F=∑(i,j)∈Ff(i,j)m1{i}⊗m1{j} lies in ℓ∞(I)⊙ℓ∞(J), has norm at most ∥f∥∞, and agrees with mf on δ(i,j) once (i,j)∈F. By Theorem 5.2(3), mf∈ℓ∞(I)⊗ˉℓ∞(J).
Example 5.4 (zero spaces). If K=0, then H⊗K=0 and M⊗ˉN={0}=B(0). The amplification x↦x⊗1K is then the zero map, which is not injective when H=0. This is the only way injectivity fails in Theorem 5.2(4). For instance, for a locally compact group G, the amplification x↦x⊗1 with K=L2(G) is injective, because L2(G)=0: Haar measure charges nonempty open sets.
Example 5.5 (the norm bound in Theorem 5.2(3) is needed). On ℓ2(N) with basis (δk)k≥1, let Tnξ=n⟨ξ,δn⟩δ1. For each k, Tnδk=0 once n>k, so Tn→0 on the total set {δk}. But for ζ=∑kk−1δk we have Tnζ=δ1 for every n. So Tn does not tend to 0 strongly, and ∥Tn∥=n is unbounded. Taking K=C, this is an example in B(H)⊗ˉB(C)=B(H⊗C).
6. Reduced and induced algebras
For a set S⊆B(H) and a projection e, write Se={ese∣eH:s∈S}⊆B(eH). For a von Neumann algebra M the subscript is used in two cases. If e∈M, Me is the reduced algebra: eMe acting on eH. If e′∈M′, then xe′=e′xe′, so Me′={xe′∣e′H:x∈M}; this is the induced algebra, and x↦xe′=xe′∣e′H is the induction. Thus (M′)e is induced when e∈M, and (M′)e′ is reduced when e′∈M′.
Proposition 6.1. Let M be a von Neumann algebra on H.
For a projection e∈M, Me is a von Neumann algebra on eH, and (Me)′=(M′)e.
For a projection e′∈M′, Me′ is a von Neumann algebra on e′H with commutant (M′)e′={e′x′e′∣e′H:x′∈M′}. The induction is a normal unital ∗-homomorphism of M onto Me′. Let c be the projection onto the closed span [M′e′H]. Then c∈M, and the induction is injective if and only if c=1.
(Reductions of tensor products.) Let N be a von Neumann algebra on K, and e∈M, f∈N projections. Identify (e⊗f)(H⊗K) with eH⊗fK, the closure of eH⊙fK. Then
(M⊗ˉN)e⊗f=Me⊗ˉNf,(M′⊗ˉN′)e⊗f=(M′)e⊗ˉ(N′)f,(6.1)
the first a reduced algebra, the second an induced one. Applied to M′ and N′, this gives the same identities for projections e′∈M′, f′∈N′, with reduction and induction exchanged: (M′⊗ˉN′)e′⊗f′=(M′)e′⊗ˉ(N′)f′ and (M⊗ˉN)e′⊗f′=Me′⊗ˉNf′.
Proof. (1) We first identify the commutant of Me, and then deduce that Me is a von Neumann algebra.
(M′)e⊆(Me)′: each x′∈M′ commutes with e and with every exe, so x′∣eH maps eH into itself and commutes with Me.
(Me)′⊆(M′)e: let T∈B(eH) commute with Me; then T∗ does too, since Me is self-adjoint. For a1,…,an∈M and ξ1,…,ξn∈eH, let A be the operator matrix [eak∗aie]k,i acting on eH⊗Cn, and ξ=(ξi). Then ⟨Aξ,ξ⟩=∥∑iaiξi∥2, so A≥0. The entries of A commute with T and T∗, so by the commutation criterion (Proposition 2.1(3)) A commutes with C=T∗T⊗1, and hence with A1/2. Therefore
i∑aiTξi2=⟨CAξ,ξ⟩=⟨CA1/2ξ,A1/2ξ⟩≤∥T∥2i∑aiξi2.
(The first equality expands both sides, using Tξi∈eH and T(eak∗aie)=(eak∗aie)T.) So ∑aiξi↦∑aiTξi is well defined and bounded on the span of MeH. Extend it by continuity to [MeH] and by 0 on [MeH]⊥; call the result x′. Both subspaces are invariant under the self-adjoint set M, and x′b=bx′ holds on each of them for b∈M. So x′∈M′. Taking n=1 and a1=1 gives x′∣eH=T.
Me is a von Neumann algebra: let S∈B(eH) commute with (M′)e, and let S~ be the operator ξ↦S(eξ) on H. For x′∈M′, S~x′ξ=S(x′eξ)=x′S(eξ)=x′S~ξ, using ex′=x′e. So S~∈M′′=M, and S=eS~e∣eH∈Me. Hence (Me)′′=((M′)e)′⊆Me.
(2) Apply (1) to the von Neumann algebra M′ and e′∈M′. The reduced algebra (M′)e′ is a von Neumann algebra with commutant (M′′)e′=Me′. So Me′=((M′)e′)′ is a von Neumann algebra, with commutant (M′)e′. The induction is multiplicative because e′ commutes with M, and unital. It is normal because ∑n⟨xe′ζn,ζn′⟩, for ζn,ζn′∈e′H, is an element of B(H)∗ evaluated at x. The subspace [M′e′H] is invariant under the self-adjoint set M′, so c∈M′′=M. If xe′=0 with x∈M, then xa′e′=a′xe′=0 for a′∈M′, so xc=0; hence c=1 forces x=0. If c=1, then 1−c is a nonzero element of M with (1−c)e′=0, because e′H⊆[M′e′H].
(3) The range of e⊗f is the closed span of the vectors eξ⊗fη, a copy of eH⊗fK. On it, (e⊗f)(x⊗y)(e⊗f) acts as (exe∣eH)⊗(fyf∣fK). The compression Φ(X)=(e⊗f)X(e⊗f)∣eH⊗fK is normal and maps M⊙N onto Me⊙Nf. By the normal extension principle (Proposition 4.3(ii)), Φ(M⊗ˉN)⊆Me⊗ˉNf. Conversely, e⊗f∈M⊗ˉN, so Φ(M⊗ˉN)=(M⊗ˉN)e⊗f is a von Neumann algebra by (1), and it contains Me⊙Nf; hence it contains Me⊗ˉNf. For the second identity, e⊗f commutes with M′⊙N′, hence lies in (M′⊗ˉN′)′. The induction by e⊗f is a normal ∗-homomorphism on M′⊗ˉN′ that maps x′⊗y′ to xe′⊗yf′. Its range is a von Neumann algebra by (2), and the same two inclusions follow. □
7. Tensor products with B(K): commutants and matrix units
Proposition 7.1. For a von Neumann algebra M⊆B(H) and any Hilbert space K,
(M⊗1)′=M′⊗ˉB(K),(M⊗ˉB(K))′=M′⊗1.(7.1)
Proof. By the commutation criterion (2.1), (M⊗1)′={X:Xjk∈M′}. This is M′⊗ˉB(K) by (5.2) applied to M′. Also by (5.2), M⊗ˉB(K)=(M′⊗1)′. So its commutant is (M′⊗1)′′, which is M′′′⊗1=M′⊗1 by Theorem 5.2(4) with S=M′. □
These are the cases N=C1 and N=B(K) of the commutation theorem (Theorem 11.4). They need only the matrix calculus.
Definition 7.3. A matrix unit in a von Neumann algebra M is a family (wij)i,j∈I in M with wij∗=wji, wijwkl=δjkwil and ∑iwii=1, the sum taken strongly.
The wii are mutually orthogonal projections, so the sum makes sense.
Proposition 7.4. Let (wij) be a matrix unit in M⊆B(H) with I=∅. Fix i0∈I and put e=wi0i0. Let (εi) be the standard basis of ℓ2(I). The formula U(ξ⊗εi)=wii0ξ, for ξ∈eH, defines a unitary U:eH⊗ℓ2(I)→H, and
U∗MU=Me⊗ˉB(ℓ2(I)).(7.2)
Proof. Put wi=wii0. Then wi∗wj=wi0iwji0=δije and wiwi∗=wii. So wi is a partial isometry with initial projection e and final projection wii, and ⟨wiξ,wjξ′⟩=δij⟨ξ,ξ′⟩ for ξ,ξ′∈eH. Hence U maps each column eH⊗εi isometrically onto wiiH, and these ranges are mutually orthogonal with ∑iwii=1. With the column expansion (1.2), U is unitary. Let Ri be the columns of eH⊗ℓ2(I), so that URi=wi∣eH. For x∈M, the entries of U∗xU are Rj∗U∗xURk=(wj∗xwk)∣eH=(ewi0jxwki0e)∣eH∈Me. Conversely, let X have entries Xjk=(eyjke)∣eH with yjk∈M. For finite F⊆I, U(∑j,k∈FRjXjkRk∗)U∗=∑j,k∈Fwji0eyjkewi0k∈M. These operators are bounded by ∥X∥ and converge strongly to UXU∗, by the truncation statement, Proposition 2.1(5). M is strongly closed, so UXU∗∈M. Hence U∗MU={X:Xjk∈Me}, which is Me⊗ˉB(ℓ2(I)) by (5.2); Me is a von Neumann algebra by Proposition 6.1(1). □
8. Maps between tensor products and normal homomorphisms
Let M,N,P be von Neumann algebras on H,K,L. For X∈B(H⊗K) and Y∈B(K⊗L) put X12=X⊗1L and Y23=1H⊗Y on H⊗K⊗L. For Z∈B(H⊗L) let F:H⊗L⊗K→H⊗K⊗L be the unitary ξ⊗θ⊗η↦ξ⊗η⊗θ (Proposition 1.3(4)), and put Z13=F(Z⊗1K)F∗. Then (x⊗z)13=x⊗1⊗z.
Proposition 8.1.
(Associativity.) Under the identification of Proposition 1.3(4),
(M⊗ˉN)⊗ˉP=M⊗ˉ(N⊗ˉP)=(M⊗1⊗1∪1⊗N⊗1∪1⊗1⊗P)′′,(8.1)
the von Neumann algebra generated by the x⊗y⊗z. We write M⊗ˉN⊗ˉP.
(Flip.) For the flip unitary Σ:H⊗K→K⊗H, Σ(x⊗y)Σ∗=y⊗x and Σ(M⊗ˉN)Σ∗=N⊗ˉM.
(Legs.) The maps X↦X12, Y↦Y23 and Z↦Z13 are normal unital ∗-homomorphisms, injective when the added space is nonzero. They map M⊗ˉN, N⊗ˉP and M⊗ˉP into M⊗ˉN⊗ˉP.
(Spatial isomorphisms.) For unitaries U1:H→H1 and U2:K→K1,
(U1⊗U2)(M⊗ˉN)(U1⊗U2)∗=(U1MU1∗)⊗ˉ(U2NU2∗).
(Tensoring an implemented map.) Let R be a Hilbert space, V:H0→H⊗R an isometry whose range projection e′=VV∗ commutes with M⊗1R, and π(x)=V∗(x⊗1R)V for x∈M. Then π:M→B(H0) is a normal unital ∗-homomorphism. For every von Neumann algebra P on L, the map
(π⊗ι)(X)=(V⊗1L)∗X13(V⊗1L)(X∈M⊗ˉP),(8.2)
with X13 acting on H⊗R⊗L, is a normal unital ∗-homomorphism with (π⊗ι)(x⊗z)=π(x)⊗z, and it is the only normal map on M⊗ˉP with these values. Likewise (ι⊗π)(X)=(1L⊗V)∗(X⊗1R)(1L⊗V) on P⊗ˉM satisfies (ι⊗π)(z⊗x)=z⊗π(x). If π(M)⊆Q for a von Neumann algebra Q on H0, then (π⊗ι)(M⊗ˉP)⊆Q⊗ˉP and (ι⊗π)(P⊗ˉM)⊆P⊗ˉQ.
Proof. (1) The set S=M⊗1∪1⊗N is self-adjoint with S′′=M⊗ˉN by Theorem 5.2(2). Applying Theorem 5.2(2) to the pair M⊗ˉN, P with the generating sets S and P gives (M⊗ˉN)⊗ˉP=(S⊗1∪1⊗P)′′, which is the right side of (8.1). The same argument with M and T=N⊗1∪1⊗P treats M⊗ˉ(N⊗ˉP). Products of the three kinds of generators give the x⊗y⊗z.
(2) Conjugation by a unitary is a ∗-isomorphism that carries commutants to commutants, and it maps M⊙N onto N⊙M.
(3) Each map is an amplification (Proposition 3.1(2)) followed by conjugation by a unitary, so it is a normal unital ∗-homomorphism. It is injective when the added space is nonzero, by the norm identity of Proposition 2.1(1). The images of elementary tensors, such as (x⊗z)13=x⊗1⊗z, lie in M⊗ˉN⊗ˉP; the normal extension principle (Proposition 4.3(ii)) extends this to the whole algebras.
(4) Conjugation by U1⊗U2 maps x⊗y to U1xU1∗⊗U2yU2∗ and commutants to commutants.
(5) π is linear, preserves adjoints and is normal. As e′ commutes with y⊗1 and V∗V=1,
π(x)π(y)=V∗(x⊗1)e′(y⊗1)V=V∗(xy⊗1)e′V=π(xy), and π(1)=1. For (8.2), X↦X13 is a normal ∗-homomorphism by (3). For X=x⊗z, X13=(x⊗1R)⊗z commutes with e′⊗1L. By Proposition 4.3(ii), applied with the von Neumann algebra {e′⊗1}′, X13 commutes with e′⊗1 for every X∈M⊗ˉP. The computation for π then repeats with V⊗1 in place of V, so π⊗ι is multiplicative; it is normal as a composite of normal maps. On elementary tensors, (V⊗1)∗((x⊗1R)⊗z)(V⊗1)=V∗(x⊗1R)V⊗z. Uniqueness is Proposition 4.3(i) with A=M⊙P. The map ι⊗π is treated in the same way, without a flip. The range statements follow from Proposition 4.3(ii), since π(x)⊗z∈Q⊙P. □
By uniqueness, π⊗ι on M⊗ˉP depends only on π, not on the choice of R and V. The next theorem shows that every normal unital ∗-homomorphism has the form in (5).
Theorem 8.2 (normal homomorphisms). Let M⊆B(H) be a von Neumann algebra and π:M→B(L) a normal unital ∗-homomorphism. There are a Hilbert space R and an isometry V:L→H⊗R such that e′=VV∗ lies in (M⊗1R)′=M′⊗ˉB(R) and
π(x)=V∗(x⊗1R)V(x∈M).(8.3)
So π is the amplification x↦x⊗1R, followed by the induction by e′, followed by the unitary V∗:e′(H⊗R)→L. Consequently:
π(M) is a von Neumann algebra on L;
if L=0, π is injective if and only if the projection onto [(M⊗1R)′e′(H⊗R)] is 1.
Reference: Compare [Takesaki I, Theorem IV.5.5].
Proof. If L=0, take R=0. Otherwise, by Zorn's lemma, choose nonzero vectors λi∈L, i∈I, whose cyclic subspaces Li=[π(M)λi] are pairwise orthogonal, with the index set I maximal for this property. Each Li is invariant under the self-adjoint set π(M). If λ is orthogonal to every Li, then so is [π(M)λ], since ⟨π(x)λ,π(y)λi⟩=⟨λ,π(x∗y)λi⟩=0; maximality forces λ=0. So L=⨁iLi. Let R=ℓ2(I×N), the orthogonal sum of the subspaces Ri=ℓ2({i}×N). The functional ψi(x)=⟨π(x)λi,λi⟩ is positive and normal on M. By the positive case of Proposition 3.1(4), applied with the infinite-dimensional space Ri, there is ζi∈H⊗Ri⊆H⊗R with ψi(x)=⟨(x⊗1)ζi,ζi⟩. Then ∥(x⊗1)ζi∥2=ψi(x∗x)=∥π(x)λi∥2, so (x⊗1)ζi↦π(x)λi extends to a unitary Ui of [(M⊗1)ζi] onto Li. The spaces [(M⊗1)ζi]⊆H⊗Ri are mutually orthogonal. Let e′ be the projection onto their sum; it commutes with M⊗1 because the sum is invariant under this self-adjoint set. Let V:L→H⊗R be the isometry equal to Ui−1 on each Li; its range projection is e′. For x,y∈M, Vπ(x)π(y)λi=Vπ(xy)λi=(xy⊗1)ζi=(x⊗1)Vπ(y)λi. So Vπ(x)=(x⊗1)V on each Li, hence on L, and π(x)=V∗(x⊗1)V. The identity (M⊗1R)′=M′⊗ˉB(R) is (7.1).
(1) By Theorem 5.2(4), M⊗1R is a von Neumann algebra. By Proposition 6.1(2), so is its induced algebra by e′. And π(M) is the image of that induced algebra under the unitary V∗.
(2) If L=0, then R=0 and the amplification is injective. V∗ is unitary on e′(H⊗R). Apply Proposition 6.1(2) to M⊗1R. □
Corollary 8.3 (normal isomorphisms). Let π:M1→M2 be a normal ∗-isomorphism of von Neumann algebras Mk⊆B(Hk), H1=0. With R, V, e′ as in Theorem 8.2, let M=M1⊗1R on H1⊗R, e2′=e′, and e1′=1⊗q for a rank-one projection q on R. Then e1′,e2′∈M′, the projections onto [M′ek′(H1⊗R)] are both 1, M1 is spatially isomorphic to the induced algebra Me1′, M2 to Me2′, and π corresponds to (x⊗1)e1′↦(x⊗1)e2′.
The hypothesis that π is normal is automatic, because every ∗-isomorphism between von Neumann algebras is normal (fact (d) of the background section).
Proof. By (7.1), M′=M1′⊗ˉB(R). It contains 1⊗B(R), so [M′(1⊗q)(H1⊗R)]⊇H1⊗R. For e2′, use part (2) of Theorem 8.2; H2=0 because M2≅M1=0. For a unit vector g∈qR, the unitary ξ↦ξ⊗g of H1 onto H1⊗qR carries x to (x⊗1)e1′, and V∗ carries (x⊗1)e2′ to π(x). □
Corollary 8.4 (tensor products of normal homomorphisms). Let πk:Mk→Nk, k=1,2, be normal unital ∗-homomorphisms between von Neumann algebras. There is a unique normal unital ∗-homomorphism π1⊗π2:M1⊗ˉM2→N1⊗ˉN2 with (π1⊗π2)(x⊗y)=π1(x)⊗π2(y). If π1 and π2 are ∗-isomorphisms onto N1 and N2 with normal inverses, then π1⊗π2 is a ∗-isomorphism onto N1⊗ˉN2.
Proof. By (8.3), π1 has the form of Proposition 8.1(5), and π1(M1)⊆N1. So, by the range statement there, π1⊗ι:M1⊗ˉM2→N1⊗ˉM2 exists, and likewise ι⊗π2:N1⊗ˉM2→N1⊗ˉN2. Put π1⊗π2=(ι⊗π2)∘(π1⊗ι). Uniqueness is Proposition 4.3(i). In the isomorphism case, (π1−1⊗π2−1)∘(π1⊗π2) is normal and is the identity on M1⊙M2, hence on M1⊗ˉM2; likewise in the other order. □
By fact (d) of the background section, the inverses in the last sentence of Corollary 8.4 are automatically normal. The lesson does not use this fact.
9. Slice maps
A slice map integrates out one leg of a tensor product against a functional on that leg. Let M⊆B(H) and N⊆B(K) be von Neumann algebras, and let Rη, Sξ be the vector maps of Proposition 1.3(2).
Lemma 9.1. For X∈M⊗ˉN and all vectors, Sξ′∗XSξ∈N and Rη′∗XRη∈M.
Proof.1⊗N′ commutes with M⊙N, so X commutes with 1⊗y′ for y′∈N′. Also (1⊗y′)Sξ=Sξy′ and Sξ′∗(1⊗y′)=y′Sξ′∗. Hence Sξ′∗XSξy′=Sξ′∗X(1⊗y′)Sξ=y′Sξ′∗XSξ, and Sξ′∗XSξ∈N′′=N. The other case uses M′⊗1 in the same way. □
Theorem 9.2 (slice maps). Let X∈M⊗ˉN.
For every bounded linear functional ω on N, normal or not, there is a unique operator (ι⊗ω)(X)∈B(H) with
⟨(ι⊗ω)(X)ξ,ξ′⟩=ω(Sξ′∗XSξ)(ξ,ξ′∈H).(9.1)
It lies in M, ∥(ι⊗ω)(X)∥≤∥ω∥∥X∥, the map X↦(ι⊗ω)(X) is linear, and (ι⊗ω)(x⊗y)=ω(y)x. If H=0, the slice map ι⊗ω:M⊗ˉN→M has norm ∥ω∥.
(Module rules, adjoints, positivity.) For a,b∈M and c,d∈N,
(ι⊗ω)((a⊗1)X(b⊗1))=a(ι⊗ω)(X)b and (ι⊗ω)((1⊗c)X(1⊗d))=(ι⊗ωc,d♭)(X), where ωc,d♭(y)=ω(cyd). Also (ι⊗ω)(X∗)=(ι⊗ω♮)(X)∗, where ω♮(y)=ω(y∗). If ω is positive, so is ι⊗ω.
(Normality.) If ω is normal, so is ι⊗ω. More precisely, if ω=∑nωηn,ηn′∣N as in Proposition 3.1(1), then
(ι⊗ω)(X)=n∑Rηn′∗XRηn,(9.2)
a norm-convergent series whose right side is a normal map on all of B(H⊗K). In particular (ι⊗ωη,η′)(X)=Rη′∗XRη. Conversely, if H=0 and ι⊗ω is normal, then ω is normal.
(The other leg.) For a bounded functional φ on M, ⟨(φ⊗ι)(X)η,η′⟩=φ(Rη′∗XRη) defines (φ⊗ι)(X)∈N, with the mirror images of (1)–(3). In particular (ωξ,ξ′⊗ι)(X)=Sξ′∗XSξ, and (φ⊗ι)((1⊗c)X(1⊗d))=c(φ⊗ι)(X)d for c,d∈N.
(Fubini identity.) For φ∈M∗ and ω∈N∗,
φ((ι⊗ω)(X))=ω((φ⊗ι)(X)).(9.3)
If φ=∑mωξm,ξm′∣M and ω=∑nωηn,ηn′∣N, both sides equal the absolutely convergent double series ∑m,n⟨X(ξm⊗ηn),ξm′⊗ηn′⟩.
Proof. (1) The right side of (9.1) is defined by Lemma 9.1. It is linear in ξ and conjugate-linear in ξ′, because Sξ is linear in ξ and Sξ′∗ is conjugate-linear in ξ′. It is bounded by ∥ω∥∥X∥∥ξ∥∥ξ′∥. A bounded sesquilinear form is given by a unique operator, of norm at most the bound. For a′∈M′, Sa′ξ=(a′⊗1)Sξ and Sξ′∗(a′⊗1)=Sa′∗ξ′∗, and X commutes with a′⊗1. So
⟨(ι⊗ω)(X)a′ξ,ξ′⟩=ω(Sa′∗ξ′∗XSξ)=⟨(ι⊗ω)(X)ξ,a′∗ξ′⟩=⟨a′(ι⊗ω)(X)ξ,ξ′⟩,
and (ι⊗ω)(X)∈M′′=M. Since Sξ′∗(x⊗y)Sξ=⟨xξ,ξ′⟩y, we get (ι⊗ω)(x⊗y)=ω(y)x. If H=0, then ∥(ι⊗ω)(1⊗y)∥=∣ω(y)∣ and ∥1⊗y∥=∥y∥, which gives the norm.
(2) The identities Sξ′∗(a⊗1)X(b⊗1)Sξ=Sa∗ξ′∗XSbξ and Sξ′∗(1⊗c)X(1⊗d)Sξ=cSξ′∗XSξd give the two module rules. For adjoints,
ω(Sξ′∗X∗Sξ)=ω((Sξ∗XSξ′)∗)=ω♮(Sξ∗XSξ′)=⟨(ι⊗ω♮)(X)ξ′,ξ⟩=⟨(ι⊗ω♮)(X)∗ξ,ξ′⟩.
If ω≥0 and X≥0, then ⟨(ι⊗ω)(X)ξ,ξ⟩=ω(Sξ∗XSξ)≥0.
(3) ⟨Rηn′∗XRηnξ,ξ′⟩=⟨X(ξ⊗ηn),ξ′⊗ηn′⟩=⟨Sξ′∗XSξηn,ηn′⟩. Summing over n gives ω(Sξ′∗XSξ), because Sξ′∗XSξ∈N. The series converges in norm, since ∥Rηn′∗XRηn∥≤∥X∥∥ηn∥∥ηn′∥. For ρ=∑kωζk,ζk′∈B(H)∗,
ρ(∑nRηn′∗XRηn)=∑k,n⟨X(ζk⊗ηn),ζk′⊗ηn′⟩,
and ∑k,n∥ζk⊗ηn∥2=∑k∥ζk∥2∑n∥ηn∥2<∞, likewise for the primed vectors. So the map is normal. Conversely, if ι⊗ω is normal, so is y↦(ι⊗ω)(1⊗y)=ω(y)1, because the amplification y↦1⊗y is normal (Proposition 3.1(2)); evaluating at a unit vector shows that ω is normal.
(4) Exchange the legs, or conjugate by the flip (Proposition 8.1(2)).
(5) By (9.1) and (9.2), φ((ι⊗ω)(X))=∑mω(Sξm′∗XSξm)=∑m∑n⟨X(ξm⊗ηn),ξm′⊗ηn′⟩. The terms are bounded by ∥X∥∥ξm∥∥ξm′∥∥ηn∥∥ηn′∥, which is summable over (m,n) by Cauchy–Schwarz. So the order of summation does not matter, and the same computation for the right side of (9.3) gives the same double series. □
Exercise 9.3 (slices by a functional that is not normal). Let N=ℓ∞(N) on ℓ2(N), and let ω be a state of N that vanishes on every finitely supported sequence, for instance the limit along a free ultrafilter. Let M⊆B(H) with H=0. Show that ι⊗ω:M⊗ˉN→M is positive, unital and of norm one, but not normal.
Solution. By Theorem 9.2(1) and (2), ι⊗ω is positive, has norm ∥ω∥=1, and (ι⊗ω)(1)=ω(1)1=1. The projections pn=1{1,…,n} increase strongly to 1, ω(pn)=0 and ω(1)=1. A normal functional is continuous for the strong topology on bounded sets: write it as in (3.1) and use dominated convergence, as in the proof of Proposition 3.1(2). So ω is not normal. By the converse in Theorem 9.2(3), ι⊗ω is not normal.
10. Product functionals and the predual
The support of a positive normal functional ψ=0 on a von Neumann algebra Q is the unique projection e∈Q such that ψ(x)=ψ(exe) for all x∈Q and ψ is faithful on eQe (fact (c) of the background section); put s(0)=0. Applied to x(1−e) and (1−e)x, the first condition gives ψ(x)=ψ(xe)=ψ(ex). A positive normal ψ is faithful exactly when s(ψ)=1.
Theorem 10.1. Let M⊆B(H) and N⊆B(K) be von Neumann algebras, φ∈M∗ and ω∈N∗.
(Product functionals.) The functional
φ⊗ω:=φ∘(ι⊗ω)=ω∘(φ⊗ι)(10.1)
is normal on M⊗ˉN, and it is the only normal functional with (φ⊗ω)(x⊗y)=φ(x)ω(y). Its norm is ∥φ∥∥ω∥. When φ and ω are positive, φ⊗ω is positive; when both are states, it is a state. For vector series φ=∑mωξm,ξm′∣M and ω=∑nωηn,ηn′∣N,
φ⊗ω=∑m,nωξm⊗ηn,ξm′⊗ηn′∣M⊗ˉN.
(Density.) The span of the functionals ωξ,ξ′⊗ωη,η′=ωξ⊗η,ξ′⊗η′∣M⊗ˉN is norm-dense in (M⊗ˉN)∗. So is the span of all φ⊗ω.
(Pairing with the algebraic tensor product.) For θ∈(M⊗ˉN)∗, ∥θ∥=sup{∣θ(X)∣:X∈M⊙N,∥X∥≤1}. So restriction to M⊙N embeds (M⊗ˉN)∗ isometrically in the dual of M⊙N with the operator norm, and by (2) the image of M∗⊙N∗ is dense in the image. Since (M⊗ˉN)∗ is norm-closed in the dual of M⊗ˉN (fact (b) of the background section), the image is the closure of M∗⊙N∗. This part uses Kaplansky's density theorem (fact (a)).
(Faithfulness and supports.) If φ and ω are faithful and positive, φ⊗ω is faithful. For positive φ,ω, s(φ⊗ω)=s(φ)⊗s(ω). Hence, if H=0=K, φ⊗ω is faithful exactly when φ and ω are.
Proof. (1) Both composites are normal by Theorem 9.2(3) and (4), and they are equal by the Fubini identity (9.3). On x⊗y the value is φ(ω(y)x)=φ(x)ω(y). Uniqueness is Proposition 4.3(i) with A=M⊙N. By Theorem 9.2(1), ∣φ((ι⊗ω)(X))∣≤∥φ∥∥ω∥∥X∥. Conversely ∣(φ⊗ω)(x⊗y)∣=∣φ(x)∣∣ω(y)∣ and ∥x⊗y∥=∥x∥∥y∥; suprema over the unit balls give ∥φ⊗ω∥≥∥φ∥∥ω∥. Positivity follows from Theorem 9.2(2), and (φ⊗ω)(1)=φ(1)ω(1). The vector formula is Theorem 9.2(5).
(2) Let θ∈(M⊗ˉN)∗. By Proposition 3.1(1), θ is the restriction of some ρ=∑kωζk,ζk′ with ζk,ζk′∈H⊗K. The tail ∑k>nωζk,ζk′ has norm at most (∑k>n∥ζk∥2)1/2(∑k>n∥ζk′∥2)1/2, which tends to 0. For k≤n, approximate ζk and ζk′ by vectors α,α′∈H⊙K, using ∥ωζ,ζ′−ωα,α′∥≤∥ζ−α∥∥ζ′∥+∥α∥∥ζ′−α′∥. By sesquilinearity, ωα,α′ is a finite sum of functionals ωξ⊗η,ξ′⊗η′, and each of these equals ωξ,ξ′⊗ωη,η′ on M⊗ˉN by (1).
(3) The inequality ≥ is clear. M⊗ˉN is the weak closure of M⊙N (Theorem 5.2(1)). By Kaplansky's density theorem, every X∈M⊗ˉN with ∥X∥≤1 is the strong limit of a net Xα∈M⊙N with ∥Xα∥≤1. With θ written as in (2),
∣θ(Xα−X)∣≤(∑k∥(Xα−X)ζk∥2)1/2(∑k∥ζk′∥2)1/2.
Each term tends to 0 and is at most 4∥ζk∥2, so the sum tends to 0, as in the proof of Proposition 3.1(2). Hence ∣θ(X)∣ is at most the supremum over the unit ball of M⊙N. The remaining sentences follow from this, from (2), and from the closedness of the predual.
(4) Faithfulness. Let X∈M⊗ˉN, X≥0, with (φ⊗ω)(X)=0. Then (φ⊗ι)(X)≥0 and ω((φ⊗ι)(X))=0; as ω is faithful, (φ⊗ι)(X)=0. For η∈K, the Fubini identity (9.3) with the normal functional ωη gives φ((ι⊗ωη)(X))=⟨(φ⊗ι)(X)η,η⟩=0, and (ι⊗ωη)(X)≥0; so (ι⊗ωη)(X)=0. Hence ⟨X(ξ⊗η),ξ⊗η⟩=⟨(ι⊗ωη)(X)ξ,ξ⟩=0, that is X1/2(ξ⊗η)=0, for all ξ,η. Since product vectors are total (Proposition 1.3(1)), X=0.
Supports. Let e=s(φ), f=s(ω) and p=e⊗f∈M⊗ˉN. If φ=0 or ω=0, both sides are 0. Otherwise:
X↦(φ⊗ω)(pXp) and φ⊗ω are normal and agree on each x⊗y, since φ(exe)ω(fyf)=φ(x)ω(y). By Proposition 4.3(i) they agree on M⊗ˉN.
φ⊗ω is faithful on p(M⊗ˉN)p. Let Y≥0 there with (φ⊗ω)(Y)=0. Since Y=(1⊗f)Y(1⊗f), Theorem 9.2(4) gives (φ⊗ι)(Y)=f(φ⊗ι)(Y)f. This is a positive element of fNf on which ω vanishes, so it is 0. For η∈K, (ι⊗ωη)(Y)=e(ι⊗ωη)(Y)e≥0 by Theorem 9.2(2), and φ vanishes on it by (9.3); so it is 0. Then Y=0 as in the first paragraph.
p=0 and φ⊗ω=0.
By the uniqueness of the support, s(φ⊗ω)=p. Finally, let H=0=K. If e=1, pick ξ=0 with eξ=0 and any η=0; then (e⊗f)(ξ⊗η)=0=ξ⊗η. So e⊗f=1 forces e=1, and likewise f=1. □
The proof of (4) does not use the commutation theorem of Section 11.
Example 10.2 (matrices: partial traces and product traces). Let H=Cm, K=Cn, and let Trn be the trace on B(Cn); it is normal, with norm n. For X∈B(Cm⊗Cn), (9.2) with Trn=∑jωfj,fj gives the partial trace (ι⊗Trn)(X)=∑jXjj. Its norm as a map is n=∥Trn∥, attained at X=1, as Theorem 9.2(1) predicts. The product functional Trm⊗Trn is Trmn, since both are normal and agree on elementary tensors (uniqueness in Theorem 10.1(1)); its norm is mn=∥Trm∥∥Trn∥.
11. The commutation theorem and its consequences
In this section ⟨ξ,η⟩R=Re⟨ξ,η⟩. It is a real inner product with the same norm, and it makes H a real Hilbert space. For a real subspace X⊆H, X⊥ is its real-orthogonal complement; X⊥⊥ is the closure X, and (iX)⊥=iX⊥. If X⊥Y and X+Y is dense, then X⊥=Y: indeed X+Y is closed, as an orthogonal sum of closed subspaces, and dense, so it is H. For a set S of operators, Sh is its self-adjoint part.
Lemma 11.1. If a,b∈B(H) are commuting self-adjoint operators and ξ∈H, then aξ⊥ibξ in the real sense. In particular Mhξ⊥iMh′ξ for a von Neumann algebra M.
Proof.⟨aξ,ibξ⟩=−i⟨baξ,ξ⟩, and ba=ab is self-adjoint, so ⟨baξ,ξ⟩ is real. □
Lemma 11.2. Let M be a von Neumann algebra on H with a cyclic vector ξ0, that is, [Mξ0]=H. Let A⊆M and B⊆M′ be ∗-subalgebras.
(a) If Ahξ0+iBhξ0 is dense in H, then A′′=M and B′′=M′.
(b) (Rieffel–van Daele.) Mhξ0+iMh′ξ0 is dense in H, and (Mhξ0)⊥ is the closure of iMh′ξ0.
(c) If 1∈A, 1∈B, A′′=M and B′′=M′, then Ahξ0+iBhξ0 is dense in H.
Proof. (a) Put X=Ahξ0 and Y=iBhξ0. By Lemma 11.1, X⊥Y, so X⊥=Y and Y⊥=X.
Step 1: Aξ0 is dense. By Lemma 11.1, Mhξ0⊥Y, so Mhξ0⊆X. Hence Mξ0=Mhξ0+iMhξ0 lies in the closure of Aξ0.
Step 2. Let b∈(A′)h. By Lemma 11.1, ibξ0∈X⊥=Y, so there are bn∈Bh with bnξ0→bξ0. For c∈B′ and x,y∈A,
⟨cbxξ0,yξ0⟩=nlim⟨cxbnξ0,yξ0⟩=nlim⟨bncxξ0,yξ0⟩=nlim⟨cxξ0,ybnξ0⟩=⟨cxξ0,byξ0⟩=⟨bcxξ0,yξ0⟩.
Here bx=xb and by=yb because b∈A′; bn∈M′ commutes with x,y∈M; c∈B′ commutes with bn∈B; and bn, b are self-adjoint. By Step 1, cb=bc. So (A′)h⊆B′′, and A′⊆B′′ because A′ is spanned by its self-adjoint part.
Step 3. From A⊆M and B⊆M′ we get M′⊆A′ and B′′⊆M′. So A′⊆B′′⊆M′⊆A′: all are equal, A′′=M′′=M, and B′′=M′.
(b) Let η0 be real-orthogonal to Mhξ0+iMh′ξ0. On H⊗C2 with basis ε1,ε2, let π(x)=x⊗1. By (7.1) and (5.2), π(M)′=M′⊗ˉB(C2) consists of the 2×2 matrices with entries in M′. Let ζ0=ξ0⊗ε1+η0⊗ε2, and let P∈π(M)′ be the projection onto [π(M)ζ0], with entries p,r,r∗,q∈M′; then 0≤p≤1. The first component of Pζ0=ζ0 is
pξ0+rη0=ξ0.(α)
For a∈Mh, ⟨aξ0,η0⟩ is purely imaginary, so ⟨aξ0,η0⟩=−⟨aξ0,η0⟩=−⟨aη0,ξ0⟩. Both sides are complex-linear in a, so this holds for all a∈M. It says that η0⊗ε1+ξ0⊗ε2 is orthogonal to every π(a)ζ0. So P annihilates it, and the first component gives
pη0+rξ0=0.(β)
For b∈Mh′, Re⟨ibξ0,η0⟩=0, so ⟨bξ0,η0⟩ is real and equals ⟨bη0,ξ0⟩. By linearity,
⟨bξ0,η0⟩=⟨bη0,ξ0⟩(b∈M′).(γ)
By (β), (γ) for b=r, and (α),
0≤⟨pη0,η0⟩=−⟨rξ0,η0⟩=−⟨rη0,ξ0⟩=−⟨(1−p)ξ0,ξ0⟩≤0.
So p1/2η0=0 and (1−p)1/2ξ0=0, hence pη0=0 and (1−p)ξ0=0. A cyclic vector for M is separating for M′: if b∈M′ and bξ0=0, then bMξ0=Mbξ0=0, so b=0. So p=1, and η0=pη0=0. This proves the density. The statement about (Mhξ0)⊥ follows from Lemma 11.1 and the remark at the start of this section.
(c) By the bicommutant theorem (Theorem 4.2), A is weakly dense in M. If aα∈A tend weakly to x∈Mh, then (aα+aα∗)/2∈Ah tend weakly to x, and (aα+aα∗)ξ0/2 tends weakly to xξ0 in H. A closed real subspace Z of H is weakly closed: if v∈/Z and u=v−PZv, with PZ the real orthogonal projection, then ⟨v,u⟩R=∥u∥2>0 while ⟨z,u⟩R=0 on Z. Hence Mhξ0 lies in the closure of Ahξ0. Likewise Mh′ξ0 lies in the closure of Bhξ0. Apply (b). □
Remark. The hypothesis of (a) forces A and B to be nondegenerate. Step 1 gives [Aξ0]=H. Step 2 with b=1 gives ξ0∈Bhξ0⊆[BH]. The projection onto [BH] lies in M′, because [BH] is invariant under M, and it fixes ξ0, which is separating for M′; so it is 1. Some condition of this kind is needed in (c): on H=C with M=M′=C, A={0} and B=M′ satisfy A′′=M and B′′=M′, but Ahξ0+iBhξ0=iRξ0 is not dense.
Lemma 11.3. Let X⊆H and Y⊆K be real subspaces such that X+iX is dense in H and Y+iY is dense in K. Let X⊙Y be the real span of the ξ⊗η with ξ∈X, η∈Y. Then X⊙Y+i(X⊥⊙Y⊥) is dense in H⊗K.
Proof. Let ζ be real-orthogonal to X⊙Y and to i(X⊥⊙Y⊥). For fixed ξ, η↦⟨ζ,ξ⊗η⟩R is a bounded real-linear functional on K. So there is a unique tξ∈K with ⟨tξ,η⟩R=⟨ζ,ξ⊗η⟩R for all η. The map t:H→K is real-linear and bounded. It is conjugate-linear: ⟨t(iξ),η⟩R=⟨ζ,ξ⊗iη⟩R=⟨tξ,iη⟩R=⟨−itξ,η⟩R. Let t′ be its real adjoint, ⟨t′η,ξ⟩R=⟨η,tξ⟩R; it is conjugate-linear too. So T=t′t is complex-linear, and
⟨Tξ,ξ⟩=⟨Tξ,ξ⟩R+i⟨Tξ,iξ⟩R=∥tξ∥2+i⟨tξ,−itξ⟩R=∥tξ∥2.
So T≥0. The two orthogonality hypotheses say:
⟨tξ,η⟩R=0 for ξ∈X, η∈Y; so t′Y⊆X⊥, and t′Y⊆X⊥;
⟨tξ,iη⟩R=⟨ζ,i(ξ⊗η)⟩R=0 for ξ∈X⊥, η∈Y⊥; so tX⊥⊆(iY⊥)⊥=iY.
Hence TX⊥⊆t′(iY)=−it′(Y)⊆iX⊥, and T2X⊥⊆T(iX⊥)=iT(X⊥)⊆X⊥. T is a norm limit of real polynomials in T2 (approximate s uniformly on [0,∥T∥2]), and X⊥ is closed, so TX⊥⊆X⊥. Thus TX⊥⊆X⊥∩iX⊥={0}: a vector in both is real-orthogonal to X and to iX, hence to the dense set X+iX. So ∥tξ∥2=⟨Tξ,ξ⟩=0 for ξ∈X⊥. Then ⟨t′η,ξ⟩R=⟨η,tξ⟩R=0 for η∈K and ξ∈X⊥, so t′K⊆X⊥⊥=X. With the first bullet, t′Y⊆X∩X⊥={0}. Hence ⟨tξ,η⟩R=⟨ξ,t′η⟩R=0 for all ξ∈H and η∈Y, that is tH⊆Y⊥. As t(iξ)=−itξ and iH=H, also tH⊆iY⊥. By density of Y+iY, Y⊥∩iY⊥={0}, so t=0. Thus Re⟨ζ,ξ⊗η⟩=0 for all ξ,η; replacing ξ by iξ gives ⟨ζ,ξ⊗η⟩=0, and ζ=0 because product vectors are total (Proposition 1.3(1)). □
Theorem 11.4 (commutation theorem). For von Neumann algebras M⊆B(H) and N⊆B(K),
(M⊗ˉN)′=M′⊗ˉN′.(11.1)
Reference: Compare [Takesaki I, Lemmas IV.5.7–IV.5.8 and Theorem IV.5.9].
Proof.Step 0.M′⊙N′ commutes with M⊙N. So M′⊗ˉN′⊆(M⊗ˉN)′ and M⊗ˉN⊆(M′⊗ˉN′)′.
Step 1: cyclic vectors. Let ξ0 be cyclic for M and η0 cyclic for N. Put X=Mhξ0 and Y=Nhη0; then X+iX=Mξ0 and Y+iY=Nη0 are dense. By Lemma 11.2(b), X⊥ and Y⊥ are the closures of iMh′ξ0 and iNh′η0. By Lemma 11.3 and the continuity of (ξ,η)↦ξ⊗η (Proposition 1.3(1)), the real span of Mhξ0⊙Nhη0 and i(iMh′ξ0⊙iNh′η0)=i(Mh′ξ0⊙Nh′η0) is dense in H⊗K. With ζ0=ξ0⊗η0, the first set lies in (M⊗ˉN)hζ0 and the second in (M′⊗ˉN′)hζ0, since x⊗y is self-adjoint for self-adjoint x,y. So (M⊗ˉN)hζ0+i(M′⊗ˉN′)hζ0 is dense. The vector ζ0 is cyclic for M⊗ˉN: (M⊙N)ζ0 contains Mξ0⊙Nη0, which is dense by Proposition 1.3(1). Lemma 11.2(a), for the von Neumann algebra M⊗ˉN with A=M⊗ˉN and B=M′⊗ˉN′, gives M′⊗ˉN′=(M′⊗ˉN′)′′=(M⊗ˉN)′.
Step 2: the general case. Let Y′∈(M⊗ˉN)′ and X′∈(M′⊗ˉN′)′. It suffices to show X′Y′=Y′X′: then (M⊗ˉN)′⊆(M′⊗ˉN′)′′=M′⊗ˉN′, and Step 0 gives equality. It suffices in turn to show ⟨X′Y′ζ,ζ⟩=⟨Y′X′ζ,ζ⟩ for ζ=ξ⊗η. Indeed, if ⟨T(ξ⊗η),ξ⊗η⟩=0 for all ξ,η, polarization in ξ gives Rη∗TRη=0, so ⟨Sξ′∗TSξη,η⟩=0 for all ξ,ξ′,η; polarization in η gives Sξ′∗TSξ=0, and T=0 by Proposition 1.3(1).
Fix ξ,η. Let e′∈M′ and f′∈N′ be the projections onto [Mξ] and [Nη], and g′=e′⊗f′∈M′⊗ˉN′. Then g′ζ=ζ. The induced algebras Me′ and Nf′ have the cyclic vectors ξ and η, and by Proposition 6.1(2) their commutants are (M′)e′ and (N′)f′. Step 1 gives (Me′⊗ˉNf′)′=(M′)e′⊗ˉ(N′)f′ on g′(H⊗K)=e′H⊗f′K.
g′∈(M⊗ˉN)′. By Proposition 6.1(1), the reduced algebra of (M⊗ˉN)′ by g′ is the commutant of the induced algebra (M⊗ˉN)g′, which is Me′⊗ˉNf′ by (6.1). So B:=g′Y′g′∣g′(H⊗K)∈(Me′⊗ˉNf′)′.
g′ lies in M′⊗ˉN′, the commutant of the von Neumann algebra (M′⊗ˉN′)′. By Proposition 6.1(2), the induced algebra of (M′⊗ˉN′)′ by g′ is the commutant of the reduced algebra (M′⊗ˉN′)g′=(M′)e′⊗ˉ(N′)f′ (6.1). By Step 1 that commutant is Me′⊗ˉNf′. So A:=X′g′∣g′(H⊗K)∈Me′⊗ˉNf′.
Hence AB=BA. Since X′ commutes with g′, g′X′Y′g′=(g′X′g′)(g′Y′g′) and g′Y′X′g′=(g′Y′g′)(g′X′g′). As ζ=g′ζ, ⟨X′Y′ζ,ζ⟩=⟨ABζ,ζ⟩=⟨BAζ,ζ⟩=⟨Y′X′ζ,ζ⟩. □
Corollary 11.5. Let Mk,Nk be von Neumann algebras on Hk (k=1,2), and let ∨ denote the von Neumann algebra generated.
(Joins.) (M1⊗ˉM2)∨(N1⊗ˉN2)=(M1∨N1)⊗ˉ(M2∨N2). This part does not use the commutation theorem.
(Centres.) The centre of M1⊗ˉM2 is Z(M1)⊗ˉZ(M2).
(Slice criterion.) X∈B(H1⊗H2) lies in M1⊗ˉM2 if and only if Rη′∗XRη∈M1 and Sξ′∗XSξ∈M2 for all vectors; equivalently, iff (ι⊗ω)(X)∈M1 and (ω′⊗ι)(X)∈M2 for all normal functionals ω on B(H2) and ω′ on B(H1). When M2=B(H2), the first family of conditions suffices, and the commutation theorem is not needed.
Proof. (1) By Theorem 5.2(2), both sides are generated by M1⊗1, 1⊗M2, N1⊗1 and 1⊗N2; on the right use the self-adjoint sets S=M1∪N1 and T=M2∪N2, with S′′=M1∨N1 and T′′=M2∨N2.
(2) Apply (1) to the commutants and take commutants, using (11.1) and (P∨Q)′=P′∩Q′:
((M1′⊗ˉM2′)∨(N1′⊗ˉN2′))′=(M1⊗ˉM2)∩(N1⊗ˉN2) and ((M1′∨N1′)⊗ˉ(M2′∨N2′))′=(M1∩N1)⊗ˉ(M2∩N2).
(3) Take Nk=Mk′ in (2), and use (11.1) for (M1⊗ˉM2)′.
(4) Necessity is Lemma 9.1, together with Theorem 9.2(3) for the slices of B(H1)⊗ˉB(H2)=B(H1⊗H2). Conversely, the matrix entries of X along any basis of H2 lie in M1, so X commutes with M1′⊗1 by the commutation criterion (Proposition 2.1(3)). Rows give X∈(1⊗M2′)′. So X∈(M1′⊗1∪1⊗M2′)′=(M1′⊗ˉM2′)′, which is M1⊗ˉM2 by (11.1). For M2=B(H2), use (5.2) instead. □
Part (4) is the "Fubini" description of the tensor product. It is the usual route to statements like W∈L∞(G)⊗ˉL(G) for a multiplicative unitary W. The bounded strong limits of Theorem 5.2(3) give another route, which needs neither the commutation theorem nor any countability; Example 12.2 follows it.
Exercise 11.6 (factors without the commutation theorem). Let M1⊆B(H1) and M2⊆B(H2) be factors, with H1,H2=0. Show, without the commutation theorem, that M1⊗ˉM2 is a factor.
Solution. This is the argument of B. Blackadar, Operator Algebras, III.1.5.10. Let Z be the centre of M1⊗ˉM2. Then Z commutes with M1⊗1, which lies in the algebra, and with M1′⊗1, which lies in its commutant. So the von Neumann algebra Z′ contains ((M1∪M1′)⊗1)′′=(M1∪M1′)′′⊗1, by Theorem 5.2(4). As M1 is a factor, (M1∪M1′)′=M1′∩M1=C1, so (M1∪M1′)′′=B(H1). Likewise Z′⊇1⊗B(H2). By Theorem 5.2(2) and (5), Z′ contains B(H1)⊗ˉB(H2)=B(H1⊗H2). Hence Z⊆B(H1⊗H2)′=C1.
Exercise 11.7 (maximal abelian subalgebras). Let Ak⊆Mk be von Neumann subalgebras with Ak′∩Mk=Ak (k=1,2). Show that (A1⊗ˉA2)′∩(M1⊗ˉM2)=A1⊗ˉA2, and that A1⊗ˉA2 is abelian.
Solution. Each Ak is abelian, since Ak=Ak′∩Mk⊆Ak′. So A1⊙A2 is commutative, and for a commutative set S, S⊆S′ gives S′′⊆S′=(S′′)′; so A1⊗ˉA2 is abelian. By (11.1), (A1⊗ˉA2)′=A1′⊗ˉA2′. By Corollary 11.5(2), (A1′⊗ˉA2′)∩(M1⊗ˉM2)=(A1′∩M1)⊗ˉ(A2′∩M2)=A1⊗ˉA2.