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.
A best rank-two CP approximation need not exist
Statement refuted
Every finite tensor has a best Frobenius approximation among tensors of CP rank at most two.
Facts & Assumptions
Given: Real three-way tensors in , with standard basis in each mode.
Canonical polyadic rank: CP rank is the least number of elementary tensors whose sum is the tensor; a rank-at-most-two tensor is a sum of at most two elementary tensors.
Counterexample
Set . It has an explicit three-term CP expression, so its CP rank is at most three. Its first-mode slices are and . They are linearly independent, and .
Suppose had a two-term expression . The first-mode slices being independent forces to be independent. If or were dependent, every linear combination of the two slices would have matrix rank at most one, so its determinant would vanish identically, contrary to . Thus both pairs are independent. For any , the pencil slice is . Its determinant is a nonzero constant times . Independence of makes the two linear factors nonproportional, so this determinant has two distinct zero lines; it cannot equal , which vanishes on the repeated line . This contradiction proves , hence it is exactly three.
For every , define . This is a difference of two elementary tensors, with the scalar absorbed into one factor, so its CP rank is at most two. Expanding gives , so . Thus the infimal rank-two approximation error is zero. Since step 2.1 shows itself has rank three, no rank-at-most-two tensor attains that infimum.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · one level
2 results 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
- Tamara G. Kolda and Brett W. Bader, Tensor Decompositions and Applications, SIAM Review 51(3) (2009), §3.3 (standard reference, not scraped)
- Mi-Song Dupuy, Tensor trains for high-dimensional problems, full lecture notes, Chapter 1, §1.2.3 (2026) (standard reference, not scraped)