How statement and proof provenance work
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Tensor train compression and truncation error — Examples
1 · Prerequisites
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tensor train compression and truncation error
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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- 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
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.
A separable grid tensor has TT rank one
Example
For nonzero vectors , consider the separable grid tensor
Verification
Given: and nonzero vectors .
[F1] Finite tensor arrays and the Frobenius inner product: a cut unfolding groups the first 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 is .
At cut , the unfolding is the outer product of the vector with entries and the vector with entries . Both vectors are nonzero, so this matrix has rank one.
Set for each core. Their contraction is , 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.
Two TT truncations realize the root-sum-square bound
Example
Let have nonzero entries , , and , with all other entries zero. Apply sequential TT-SVD with rank cap one at both cuts.
Verification
Given: The tensor and cap tuple above.
[F1] Finite tensor arrays and the Frobenius inner product: the cut- unfolding groups the first 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.
With columns ordered by , the first unfolding is . Its rows are orthogonal with norms and , so the leading singular value is and the first discarded tail is . The retained left vector is , and the remainder reshaped as a matrix is .
The second remainder has singular values and , hence its rank-one truncation discards and returns the tensor . The omitted entries of are and , which are orthogonal and have squared norms and . Therefore , realizing equality in [F3].
A diagonal tensor has large minimal TT ranks
Example
For integers and , let
Verification
Given: The tensor above with and .
[F1] Finite tensor arrays and the Frobenius inner product: a cut unfolding has row tuple and column tuple .
[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.
For each cut , only the row tuples and the matching column tuples contain nonzero entries of . The resulting matrix has ones in distinct rows and columns and zeros elsewhere, so its rank is exactly .
By [F3], every exact TT representation has bond size at least at every cut. Such a representation is explicit: the first core enforces , each interior core enforces , and the last core enforces . Contracting these Kronecker-delta entries yields exactly when all mode indices are the same and otherwise, which is the stated tensor.
Sources
- Tamara G. Kolda and Brett W. Bader, Tensor Decompositions and Applications, SIAM Review 51(3) (2009), §3.3
- Mi-Song Dupuy, Tensor trains for high-dimensional problems, full lecture notes, Chapter 1, §1.2.3 (2026)
- Ivan V. Oseledets, Tensor-Train Decomposition, SIAM Journal on Scientific Computing 33(5) (2011), §2
- Mi-Song Dupuy, Tensor trains for high-dimensional problems, full lecture notes, Chapter 1, Example 1.2.3 (2026)
- Ivan V. Oseledets, Tensor-Train Decomposition, SIAM Journal on Scientific Computing 33(5) (2011), §2, Theorem 2.2
- Mi-Song Dupuy, Tensor trains for high-dimensional problems, full lecture notes, Chapter 1, §1.2.4 (2026)
- Mi-Song Dupuy, Tensor trains for high-dimensional problems, full lecture notes, Chapter 1, §1.2.3, Theorem 1.2.12 (2026)