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Tensor train compression and truncation error — Examples

1 · Prerequisites

2 · Summary

These examples make the abstract bounds concrete: a separable grid has TT rank one; a small tensor realizes equality in the two-cut root-sum-square error estimate; and a diagonal order-d tensor has every exact TT bond rank equal to its mode size. The final counterexample expands a convergent rank-two CP sequence and proves by a matrix-pencil determinant argument that its limit has CP rank three, so a best rank-two approximation need not be attained.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

CounterexampleConstruction: AI-generatedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

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 R2×2×2, with standard basis e1,e2 in each mode.

[F1]

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

1.1givenF1constructalgebra

Set W=e2⊗e1⊗e1+e1⊗e2⊗e1+e1⊗e1⊗e2. It has an explicit three-term CP expression, so its CP rank is at most three. Its first-mode slices are W1=(0110) and W2=(1000). They are linearly independent, and det⁡(xW1+yW2)=−x2.

2.1F1step 1.1algebra

Suppose W had a two-term expression a⊗b⊗c+a′⊗b′⊗c′. The first-mode slices being independent forces a,a′ to be independent. If b,b′ or c,c′ were dependent, every linear combination of the two slices would have matrix rank at most one, so its determinant would vanish identically, contrary to det⁡(xW1+yW2)=−x2. Thus both pairs are independent. For any x,y, the pencil slice is (xa1+ya2)bcT+(xa1′+ya2′)b′c′T. Its determinant is a nonzero constant times (xa1+ya2)(xa1′+ya2′). Independence of a,a′ makes the two linear factors nonproportional, so this determinant has two distinct zero lines; it cannot equal −x2, which vanishes on the repeated line x=0. This contradiction proves rank⁡CP(W)>2, hence it is exactly three.

3.1F1step 2.1algebra∎

For every ε>0, define Wε=ε−1((e1+εe2)⊗3−e1⊗3). 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 Wε=W+ε(e2⊗e2⊗e1+e2⊗e1⊗e2+e1⊗e2⊗e2)+ε2e2⊗3, so ∥Wε−W∥F→0. Thus the infimal rank-two approximation error is zero. Since step 2.1 shows W itself has rank three, no rank-at-most-two tensor attains that infimum.

ExampleConstruction: AI-generatedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A separable grid tensor has TT rank one

Example

For nonzero vectors u(k)∈Rnk, consider the separable grid tensor A=u(1)⊗⋯⊗u(d).

Verification

Given: d≥2 and nonzero vectors u(k)∈Rnk.

[F1] Finite tensor arrays and the Frobenius inner product: a cut unfolding groups the first k indices into rows and the remaining indices into columns.

[F2] Tensor train cores, bond ranks, and canonical gauges: TT cores contract over their shared bond indices.

[F3] Exact TT ranks and root-sum-square truncation error: the exact minimal bond size at cut k is rank⁡(A⟨k⟩).

1.1F1givenconstructalgebra

At cut k, the unfolding is the outer product of the vector with entries ∏j=1kuij(j) and the vector with entries ∏j=k+1duij(j). Both vectors are nonzero, so this matrix has rank one.

2.1F2F3step 1.1algebra∎

Set Gk(1,ik,1)=uik(k) for each core. Their contraction is ∏kuik(k)=Ai1,…,id, so this is an exact TT with every internal bond size one. By [F3] these sizes are minimal; full-rank TT-SVD therefore also returns this exact rank-one structure.

ExampleConstruction: AI-generatedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

Two TT truncations realize the root-sum-square bound

Example

Let A∈R2×2×2 have nonzero entries A111=3, A122=1, and A212=2, with all other entries zero. Apply sequential TT-SVD with rank cap one at both cuts.

Verification

Given: The tensor and cap tuple (1,1) above.

[F1] Finite tensor arrays and the Frobenius inner product: the cut-k unfolding groups the first k indices into rows, the remaining indices into columns, and a reindexing preserves Frobenius norm.

[F2] Sequential tensor-train SVD: each step keeps the leading singular vectors up to its rank cap and records the discarded tail norm.

[F3] Exact TT ranks and root-sum-square truncation error: the total TT-SVD error is bounded by the root sum of squared cut tails.

1.1F1F2givenalgebra

With columns ordered by (i2,i3)=(1,1),(1,2),(2,1),(2,2), the first unfolding is (30010200). Its rows are orthogonal with norms 10 and 2, so the leading singular value is 10 and the first discarded tail is δ1=2. The retained left vector is (1,0)T, and the remainder Σ1V1∗ reshaped as a 2×2 matrix is (3001).

2.1F2F3step 1.1algebra∎

The second remainder has singular values 3 and 1, hence its rank-one truncation discards δ2=1 and returns the tensor A^=3e1⊗e1⊗e1. The omitted entries of A−A^ are e1⊗e2⊗e2 and 2e2⊗e1⊗e2, which are orthogonal and have squared norms 1 and 4. Therefore ∥A−A^∥F=5=δ12+δ22, realizing equality in [F3].

ExampleConstruction: AI-generatedVerification: AI-adaptedprecheck passjudge pass (gpt-6-luna)Open item page →

A diagonal tensor has large minimal TT ranks

Example

For integers d≥3 and n≥2, let A=∑j=1nej(1)⊗ej(2)⊗⋯⊗ej(d)∈Rn×⋯×n.

Verification

Given: The tensor above with d≥3 and n≥2.

[F1] Finite tensor arrays and the Frobenius inner product: a cut unfolding has row tuple (i1,…,ik) and column tuple (ik+1,…,id).

[F2] Tensor train cores, bond ranks, and canonical gauges: TT cores contract along the bond indices.

[F3] Exact TT ranks and root-sum-square truncation error: minimal exact TT bond size at a cut equals the rank of that cut unfolding.

1.1F1givenalgebra

For each cut k, only the n row tuples (j,…,j) and the matching n column tuples (j,…,j) contain nonzero entries of A⟨k⟩. The resulting matrix has n ones in distinct rows and columns and zeros elsewhere, so its rank is exactly n.

2.1F2F3step 1.1constructalgebra∎

By [F3], every exact TT representation has bond size at least n at every cut. Such a representation is explicit: the first core enforces α1=i1, each interior core enforces αk−1=ik=αk, and the last core enforces αd−1=id. Contracting these Kronecker-delta entries yields 1 exactly when all mode indices are the same and 0 otherwise, which is the stated tensor.

Sources