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TheoremStatement: Literature-sourcedProof: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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Schmidt decomposition

Statement

Let H,K be finite-dimensional complex-inner-product spaces with the tensor-product convention of Ordered tensor registers, their inner product, and wire permutations. For every Ψ∈H⊗K there are an integer r≥0, positive numbers s1,…,sr, and orthonormal families u1,…,ur∈H and v1,…,vr∈K such that

Ψ=∑j=1rsjuj⊗vj,∥Ψ∥2=∑j=1rsj2.

For Ψ=0 take r=0 and the empty sum. For any chosen ordered pair of orthonormal bases, the construction below obtains the sj as the positive singular values of the coefficient matrix of Ψ in their ordered product basis.

Facts & Assumptions

Given: Finite-dimensional complex-inner-product spaces H,K, their ordered orthonormal bases, and Ψ∈H⊗K.

[F1]

Ordered tensor registers, their inner product, and wire permutations: the ordered product basis is orthonormal and elementary-tensor pairings are products of the factor pairings.

[F2]

Dirac Notation Conversion: the root inner product is linear in its first argument.

[F3]

The proof of Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition, steps 1.1–2.1, constructs an orthonormal eigenbasis (qk) of the domain for ∣T∣, a finite orthonormal family pk=sk−1Tqk for the positive singular values sk, and zero action Tqk=0 on zero singular values. Expanding an arbitrary vector in the domain basis gives the positive-family identity Tz=∑sk>0sk⟨z,qk⟩pk; this use does not require extending the positive family in the codomain to a basis.

[F4]

Every finite-dimensional real or complex inner product space has an orthonormal basis: finite-dimensional inner-product spaces, including the zero space, have orthonormal bases.

[F5]

The standard formulas ⟨x,y⟩=∑k<nxkyk on Rn and ∑k<nxkyk‾ on Cn are inner products: the standard pairing on Cn is linear in the first argument, and its standard basis is orthonormal.

Proof

1.1givenF1F3F4F5construct

Choose ordered orthonormal bases (ei)i=1m of H and (fk)k=1n of K using [F4], and write Ψ=∑i,kcikei⊗fk by [F1]. Define the complex-linear map TC:Cn→Cm by (TCz)i=∑k=1ncikzk; its standard matrix is exactly C. Apply the positive-family construction [F3] to TC. If qj and pj are its right and positive left singular vectors, the identity there gives TCz=∑j=1rsj⟨z,qj⟩pj. Evaluating at each standard basis vector of Cn and using [F5], the (i,k) entry of this identity is cik=∑j=1rsj(pj)i(qj)k‾. This is the coefficient formula needed below, obtained from the positive singular families without a codomain basis completion.

2.1F1F2F3F5step 1.1algebra

Define uj=∑i(pj)iei and vj=∑k(qj)k‾fk. The coordinate columns of the pj are orthonormal by [F3]–[F5], so the uj are orthonormal. For the second family, ⟨vj,vℓ⟩=∑k(qj)k‾(qℓ)k=⟨qj,qℓ⟩‾=δjℓ, so it too is orthonormal. The coefficient of ei⊗fk in ∑jsjuj⊗vj is ∑jsj(pj)i(qj)k‾=cik by step 1.1, so this sum equals Ψ. If C=0, then Ψ=0 and r=0. The positive coefficients are precisely the positive singular values of C.

3.1F1step 2.1algebra∎

By [F1], the vectors uj⊗vj are orthonormal: their pairings are ⟨ui,uj⟩⟨vi,vj⟩=δij. Expanding the squared norm of the sum in step 2.1 therefore gives ∥Ψ∥2=∑jsj2, including the empty sum when Ψ=0.

Remarks

This proof uses only the SVD supplier's positive singular families and the expansion obtained from its domain eigenbasis; it does not use the separate claim that the positive left-singular family has already been extended to a full basis. All remaining choices are finite-dimensional basis or finite coordinate choices.

Depends on

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Sources