# Tucker manifold

Manifolds.TuckerType
Tucker{N, R, D, 𝔽} <: AbstractManifold{𝔽}

The manifold of $N_1 \times \dots \times N_D$ real-valued or complex-valued tensors of fixed multilinear rank $(R_1, \dots, R_D)$ . If $R_1 = \dots = R_D = 1$, this is the Segre manifold, i.e., the set of rank-1 tensors.

Representation in HOSVD format

Let $\mathbb{F}$ be the real or complex numbers. Any tensor $p$ on the Tucker manifold can be represented as a multilinear product in HOSVD [DeLathauwer2000] form

$$$p = (U_1,\dots,U_D) \cdot \mathcal{C}$$$

where $\mathcal C \in \mathbb{F}^{R_1 \times \dots \times R_D}$ and, for $d=1,\dots,D$, the matrix $U_d \in \mathbb{F}^{N_d \times R_d}$ contains the singular vectors of the $d$th unfolding of $\mathcal{A}$

Tangent space

The tangent space to the Tucker manifold at $p = (U_1,\dots,U_D) \cdot \mathcal{C}$ is [Koch2010]

$$$T_p \mathcal{M} = \bigl\{ (U_1,\dots,U_D) \cdot \mathcal{C}^\prime + \sum_{d=1}^D \bigl( (U_1, \dots, U_{d-1}, U_d^\prime, U_{d+1}, \dots, U_D) \cdot \mathcal{C} \bigr) \bigr\}$$$

where $\mathcal{C}^\prime$ is arbitrary, $U_d^{\mathrm{H}}$ is the Hermitian adjoint of $U_d$, and $U_d^{\mathrm{H}} U_d^\prime = 0$ for all $d$.

Constructor

Tucker(N::NTuple{D, Int}, R::NTuple{D, Int}[, field = ℝ])

Generate the manifold of field-valued tensors of dimensions N[1] × … × N[D] and multilinear rank R = (R[1], …, R[D]).

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Manifolds.TuckerPointType
TuckerPoint{T,D}

An order D tensor of fixed multilinear rank and entries of type T, which makes it a point on the Tucker manifold. The tensor is represented in HOSVD form.

Constructors:

TuckerPoint(core::AbstractArray{T,D}, factors::Vararg{<:AbstractMatrix{T},D}) where {T,D}

Construct an order D tensor of element type T that can be represented as the multilinear product (factors[1], …, factors[D]) ⋅ core. It is assumed that the dimensions of the core are the multilinear rank of the tensor and that the matrices factors each have full rank. No further assumptions are made.

TuckerPoint(p::AbstractArray{T,D}, mlrank::NTuple{D,Int}) where {T,D}

The low-multilinear rank tensor arising from the sequentially truncated the higher-order singular value decomposition of the D-dimensional array p of type T. The singular values are truncated to get a multilinear rank mlrank [Vannieuwenhoven2012].

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Manifolds.TuckerTVectorType
TuckerTVector{T, D} <: TVector

Tangent vector to the D-th order Tucker manifold at $p = (U_1,\dots,U_D) ⋅ \mathcal{C}$. The numbers are of type T and the vector is represented as

$$$X = (U_1,\dots,U_D) \cdot \mathcal{C}^\prime + \sum_{d=1}^D (U_1,\dots,U_{d-1},U_d^\prime,U_{d+1},\dots,U_D) \cdot \mathcal{C}$$$

where $U_d^\mathrm{H} U_d^\prime = 0$.

Constructor

TuckerTVector(C′::Array{T,D}, U′::NTuple{D,Matrix{T}}) where {T,D}

Constructs a Dth order TuckerTVector of number type T with $C^\prime$ and $U^\prime$, so that, together with a TuckerPoint $p$ as above, the tangent vector can be represented as $X$ in the above expression.

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Base.convertMethod
Base.convert(::Type{Matrix{T}}, basis::CachedBasis{𝔽,DefaultOrthonormalBasis{𝔽, TangentSpaceType},HOSVDBasis{T, D}}) where {𝔽, T, D}
Base.convert(::Type{Matrix}, basis::CachedBasis{𝔽,DefaultOrthonormalBasis{𝔽, TangentSpaceType},HOSVDBasis{T, D}}) where {𝔽, T, D}

Convert a HOSVD-derived cached basis from [Dewaele2021] of the Dth order Tucker manifold with number type T to a matrix. The columns of this matrix are the vectorisations of the embeddings of the basis vectors.

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Base.foreachFunction
Base.foreach(f, M::Tucker, p::TuckerPoint, basis::AbstractBasis, indices=1:manifold_dimension(M))

Let basis be and AbstractBasis at a point p on M. Suppose f is a function that takes an index and a vector as an argument. This function applies f to i and the ith basis vector sequentially for each i in indices. Using a CachedBasis may speed up the computation.

NOTE: The i'th basis vector is overwritten in each iteration. If any information about the vector is to be stored, f must make a copy.

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ManifoldsBase.check_pointMethod
check_point(M::Tucker{N,R,D}, p; kwargs...) where {N,R,D}

Check whether the multidimensional array or TuckerPoint p is a point on the Tucker manifold, i.e. it is a Dth order N[1] × … × N[D] tensor of multilinear rank (R[1], …, R[D]). The keyword arguments are passed to the matrix rank function applied to the unfoldings. For a TuckerPoint it is checked that the point is in correct HOSVD form.

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ManifoldsBase.check_vectorMethod
check_vector(M::Tucker{N,R,D}, p::TuckerPoint{T,D}, X::TuckerTVector) where {N,R,T,D}

Check whether a TuckerTVector X is is in the tangent space to the Dth order Tucker manifold M at the Dth order TuckerPoint p. This is the case when the dimensions of the factors in X agree with those of p and the factor matrices of X are in the orthogonal complement of the HOSVD factors of p.

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ManifoldsBase.embedMethod
embed(::Tucker{N,R,D}, p::TuckerPoint) where {N,R,D}

Convert a TuckerPoint p on the rank R Tucker manifold to a full N[1] × … × N[D]-array by evaluating the Tucker decomposition.

embed(::Tucker{N,R,D}, p::TuckerPoint, X::TuckerTVector) where {N,R,D}

Convert a tangent vector X with base point p on the rank R Tucker manifold to a full tensor, represented as an N[1] × … × N[D]-array.

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ManifoldsBase.get_basisMethod
get_basis(:: Tucker, p::TuckerPoint, basisType::DefaultOrthonormalBasis{𝔽, TangentSpaceType}) where 𝔽

An implicitly stored basis of the tangent space to the Tucker manifold. Assume $p = (U_1,\dots,U_D) \cdot \mathcal{C}$ is in HOSVD format and that, for $d=1,\dots,D$, the singular values of the $d$'th unfolding are $\sigma_{dj}$, with $j = 1,\dots,R_d$. The basis of the tangent space is as follows: [Dewaele2021]

$$$\bigl\{ (U_1,\dots,U_D) e_i \bigr\} \cup \bigl\{ (U_1,\dots, \sigma_{dj}^{-1} U_d^{\perp} e_i e_j^T,\dots,U_D) \cdot \mathcal{C} \bigr\}$$$

for all $d = 1,\dots,D$ and all canonical basis vectors $e_i$ and $e_j$. Every $U_d^\perp$ is such that $[U_d \quad U_d^{\perp}]$ forms an orthonormal basis of $\mathbb{R}^{N_d}$.

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ManifoldsBase.innerMethod
inner(M::Tucker, p::TuckerPoint, X::TuckerTVector, Y::TuckerTVector)

The Euclidean inner product between tangent vectors X and X at the point p on the Tucker manifold. This is equal to embed(M, p, X) ⋅ embed(M, p, Y).

inner(::Tucker, A::TuckerPoint, X::TuckerTVector, Y)
inner(::Tucker, A::TuckerPoint, X, Y::TuckerTVector)

The Euclidean inner product between X and Y where X is a vector tangent to the Tucker manifold at p and Y is a vector in the ambient space or vice versa. The vector in the ambient space is represented as a full tensor, i.e., a multidimensional array.

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ManifoldsBase.inverse_retractMethod
inverse_retract(M::Tucker, p::TuckerPoint, q::TuckerPoint, ::ProjectionInverseRetraction)

The projection inverse retraction on the Tucker manifold interprets q as a point in the ambient Euclidean space (see embed) and projects it onto the tangent space at to M at p.

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ManifoldsBase.manifold_dimensionMethod
manifold_dimension(::Tucker{N,R,D}) where {N,R,D}

The dimension of the manifold of $N_1 \times \dots \times N_D$ tensors of multilinear rank $(R_1, \dots, R_D)$, i.e.

$$$\mathrm{dim}(\mathcal{M}) = \prod_{d=1}^D R_d + \sum_{d=1}^D R_d (N_d - R_d).$$$
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ManifoldsBase.projectMethod
project(M::Tucker, p::TuckerPoint, X)

The least-squares projection of a dense tensor X onto the tangent space to M at p.

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ManifoldsBase.retractMethod
retract(::Tucker, p::TuckerPoint, X::TuckerTVector, ::PolarRetraction)

The truncated HOSVD-based retraction [Kressner2014] to the Tucker manifold, i.e. the result is the sequentially tuncated HOSVD approximation of $p + X$.

In the exceptional case that the multilinear rank of $p + X$ is lower than that of $p$, this retraction produces a boundary point, which is outside the manifold.

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## Literature

• DeLathauwer2000

Lieven De Lathauwer, Bart De Moor, Joos Vandewalle: "A multilinear singular value decomposition" SIAM Journal on Matrix Analysis and Applications, 21(4), pp. 1253-1278, 2000 doi: 10.1137/S0895479896305696

• Koch2010

Othmar Koch, Christian Lubic, "Dynamical Tensor approximation" SIAM Journal on Matrix Analysis and Applications, 31(5), pp. 2360-2375, 2010 doi: 10.1137/09076578X

• Vannieuwenhoven2012

Nick Vannieuwenhoven, Raf Vandebril, Karl Meerbergen: "A new truncation strategy for the higher-order singular value decomposition" SIAM Journal on Scientific Computing, 34(2), pp. 1027-1052, 2012 doi: 10.1137/110836067

• Dewaele2021

Nick Dewaele, Paul Breiding, Nick Vannieuwenhoven, "The condition number of many tensor decompositions is invariant under Tucker compression" arxiv: 2106.13034

• Kressner2014

Daniel Kressner, Michael Steinlechner, Bart Vandereycken: "Low-rank tensor completion by Riemannian optimization" BIT Numerical Mathematics, 54(2), pp. 447-468, 2014 doi: 10.1007/s10543-013-0455-z