Alphabeta Math
LemmaStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
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  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

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Trace norm of a tensor product

Statement

For finite-dimensional complex-inner-product spaces H1,K1,H2,K2 and linear maps A:H1→K1, B:H2→K2, ∥A⊗B∥1=∥A∥1 ∥B∥1. The equality includes zero maps and zero-dimensional factors.

Facts & Assumptions

Given: The finite-dimensional spaces and linear maps in the Statement.

[F1]

The trace norm is the sum of the positive singular values, with zeros contributing nothing (Trace Norm).

[F2]

Each finite-dimensional linear map has a singular-value decomposition with orthonormal left and right singular families and positive singular values (Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition).

[F3]

Tensor maps apply each factor map on elementary tensors, and the product inner product is the product of factor inner products (Tensor products of finitely many linear maps, Ordered tensor registers, their inner product, and wire permutations).

Proof

1.1givenF1F2choosealgebra

If either A or B is zero, or a domain or codomain factor is zero-dimensional, then A⊗B=0 and at least one factor trace norm is zero, so the formula holds. Otherwise choose SVDs Az=∑i=1rsi⟨z,vi⟩ui and Bw=∑j=1qtj⟨w,qj⟩pj as in [F2], with all si,tj>0.

2.1givenF1F2F3step 1.1algebra∎

By [F3], on elementary tensors (A⊗B)(z⊗w)=∑i=1r∑j=1qsitj⟨z⊗w,vi⊗qj⟩ ui⊗pj. The families (vi⊗qj)i,j and (ui⊗pj)i,j are orthonormal because their pairings are products of the factor pairings. Since elementary tensors span, this is a singular-value decomposition of A⊗B with positive singular values exactly sitj. Thus [F1] gives ∥A⊗B∥1=∑i,jsitj=(∑isi)(∑jtj)=∥A∥1∥B∥1.

Depends on

Used by

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Sources