How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- 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.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Tensor train cores, bond ranks, and canonical gauges
Definition
Let with . A tensor-train (TT) representation consists of integers , internal bond sizes , and cores such that An empty bond index set gives an empty sum, hence the zero tensor; in particular, all internal bond sizes zero represent zero. A bond size is the storage dimension contracted between cores and . The cut- unfolding groups modes against as fixed in Finite tensor arrays and the Frobenius inner product.
For an internal bond , an invertible matrix gives a gauge transformation by replacing the two adjacent cores with Their contraction is unchanged because .
The left matricization of core has row index and column index . The core is left-isometric if this matrix satisfies . The right matricization has row index and column index ; the core is right-isometric if its matrix satisfies . A TT is in mixed canonical gauge about core when cores are left-isometric and cores are right-isometric.
Diagram
The diagram shows the order-three pattern. Each box is one core, each horizontal wire is a bond index summed over in the entry formula, and each vertical leg is a free physical index. Thus leaving the physical legs open and contracting the two bond wires denotes exactly the core product defining ; the diagram is only a notation for that finite sum.
Depends on
Used by
- Sequential tensor-train SVD Algorithm
- Tensor-train rounding Algorithm
- Finite matrix-product states and Hermitian Hamiltonians Definition
- A diagonal tensor has large minimal TT ranks Example
- A separable grid tensor has TT rank one Example
- Two TT truncations realize the root-sum-square bound Example
- A periodic finite MPS has an exact open-boundary TT conversion Lemma
- Canonical MPS environments are isometric and the effective Hamiltonian is Hermitian Lemma
- Rank-revealing QR sweeps give exact mixed canonical gauge Lemma
- Exact TT ranks and root-sum-square truncation error Theorem
- Tensor-train storage and inner-product costs Theorem
Dependency tree · one level
1 result within one dependency step of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Ivan V. Oseledets, Tensor-Train Decomposition, SIAM Journal on Scientific Computing 33(5) (2011), §§2–4 (standard reference, not scraped)
- Mi-Song Dupuy, Tensor trains for high-dimensional problems, full lecture notes, Chapter 1, §§1.2.1–1.2.5 (2026) (standard reference, not scraped)