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Tensors decompositions are a class of tools for analysing datasets of high dimensionality and variety in a natural manner, with the Canonical Polyadic Decomposition (CPD) being a main pillar. While the notion of CPD is closely intertwined…

Signal Processing · Electrical Eng. & Systems 2019-11-15 Giuseppe G. Calvi , Bruno Scalzo Dees , Danilo P. Mandic

We show that for a nonnegative tensor, a best nonnegative rank-r approximation is almost always unique, its best rank-one approximation may always be chosen to be a best nonnegative rank-one approximation, and that the set of nonnegative…

Numerical Analysis · Computer Science 2016-02-16 Yang Qi , Pierre Comon , Lek-Heng Lim

We give a robust version of the celebrated result of Kruskal on the uniqueness of tensor decompositions: we prove that given a tensor whose decomposition satisfies a robust form of Kruskal's rank condition, it is possible to approximately…

Data Structures and Algorithms · Computer Science 2013-05-01 Aditya Bhaskara , Moses Charikar , Aravindan Vijayaraghavan

Third-order tensors are widely used as a mathematical tool for modeling physical properties of media in solid state physics. In most cases, they arise as constitutive tensors of proportionality between basic physics quantities. The…

Mathematical Physics · Physics 2022-11-08 Yakov Itin , Shulamit Reches

One of the main issues in computing a tensor decomposition is how to choose the number of rank-one components, since there is no finite algorithms for determining the rank of a tensor. A commonly used approach for this purpose is to find a…

Computer Vision and Pattern Recognition · Computer Science 2023-09-15 Claudio Turchetti

We prove a lower bound on the rank of tensors constructed from families of linear maps that `expand' the dimension of every subspace. Such families, called {\em dimension expanders} have been studied for many years with several known…

Combinatorics · Mathematics 2025-12-10 Zeev Dvir

Recently, many structured tensors are defined and their properties are discussed in the literature. In this paper, we introduce a new class of structured tensors, called exceptionally regular tensor, which is relevant to the tensor…

Optimization and Control · Mathematics 2015-08-27 Yong Wang , Zheng-Hai Huang , Xue-Li Bai

Starting from our previous papers [AGMO] and [ABC], we prove the existence of a non-empty Euclidean open subset whose elements are polynomial vectors with 4 components, in 3 variables, degrees, respectively, 2,3,3,3 and rank 6, which are…

Algebraic Geometry · Mathematics 2018-11-06 Elena Angelini

We study the real rank of points with respect to a real variety $X$. This is a generalization of various tensor ranks, where $X$ is in a specific family of real varieties like Veronese or Segre varieties. The maximal real rank can be…

Algebraic Geometry · Mathematics 2015-11-24 Grigoriy Blekherman , Rainer Sinn

We study the case of a real homogeneous polynomial $P$ whose minimal real and complex decompositions in terms of powers of linear forms are different. We prove that, if the sum of the complex and the real ranks of $P$ is at most $…

Algebraic Geometry · Mathematics 2013-03-12 Edoardo Ballico , Alessandra Bernardi

This paper studies the issues about tensors. Three typical kinds of tensor decomposition are mentioned. Among these decompositions, the t-SVD is proposed in this decade. Different definitions of rank derive from tensor decompositions. Based…

Numerical Analysis · Mathematics 2020-05-26 Jun Han

We lower bound the rank of a tensor by a linear combination of the ranks of three of its unfoldings, using Sylvester's rank inequality. In a similar way, we lower bound the symmetric rank by a linear combination of the symmetric ranks of…

Algebraic Geometry · Mathematics 2023-02-15 Kexin Wang , Anna Seigal

Selecting the latent dimensions (ranks) in tensor factorization is a central challenge that often relies on heuristic methods. This paper introduces a rigorous approach to determine rank identifiability in probabilistic tensor models, based…

Machine Learning · Computer Science 2026-04-03 Eliezer da Silva , Arto Klami , Diego Mesquita , Iñigo Urteaga

In this paper we study the set of tensors that admit a special type of decomposition called an orthogonal tensor train decomposition. Finding equations defining varieties of low-rank tensors is generally a hard problem, however, the set of…

Algebraic Geometry · Mathematics 2021-11-01 Pardis Semnani , Elina Robeva

We show that finding rank-$R$ decompositions of a 3D tensor, for $R\le 4$, over a fixed finite field can be done in polynomial time. However, if some cells in the tensor are allowed to have arbitrary values, then rank-2 is NP-hard over the…

Computational Complexity · Computer Science 2024-04-18 Jason Yang

We describe a new method to determine the minimality and identifiability of a Waring decomposition $A$ of a specific form (symmetric tensor) $T$ in three variables. Our method, which is based on the Hilbert function of $A$, can distinguish…

Algebraic Geometry · Mathematics 2020-06-02 Elena Angelini , Luca Chiantini

We show how methods of algebraic geometry can produce criteria for the identifiability of specific tensors that reach beyond the range of applicability of the celebrated Kruskal criterion. More specifically, we deal with the symmetric…

Algebraic Geometry · Mathematics 2022-09-02 Elena Angelini , Luca Chiantini , Nick Vannieuwenhoven

Let $X(\RR)$ be a geometrically connected variety defined over $\RR$ and such that the set of all its (also complex) points $X(\CC)$ is non-degenerate. We introduce the notion of \emph{admissible rank} of a point $P$ with respect to $X$ to…

Algebraic Geometry · Mathematics 2016-04-11 Edoardo Ballico , Alessandra Bernardi

We study the decomposability and the subdifferential of the tensor nuclear norm. Both concepts are well understood and widely applied in matrices but remain unclear for higher-order tensors. We show that the tensor nuclear norm admits a…

Optimization and Control · Mathematics 2026-03-17 Jiewen Guan , Bo Jiang , Zhening Li

Canonical Polyadic Decomposition (CPD) represents a third-order tensor as the minimal sum of rank-1 terms. Because of its uniqueness properties the CPD has found many concrete applications in telecommunication, array processing, machine…

Spectral Theory · Mathematics 2019-12-06 Ignat Domanov , Lieven De Lathauwer