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A symmetric tensor is a higher order generalization of a symmetric matrix. In this paper, we study various properties of symmetric tensors in relation to a decomposition into a sum of symmetric outer product of vectors. A rank-1 order-k…

Numerical Analysis · Mathematics 2008-09-02 Pierre Comon , Gene Golub , Lek-Heng Lim , Bernard Mourrain

We show that a tensor field of any rank integrates to zero over all broken rays if and only if it is a symmetrized covariant derivative of a lower order tensor which satisfies a symmetry condition at the reflecting part of the boundary and…

Differential Geometry · Mathematics 2020-01-24 Joonas Ilmavirta , Gabriel P. Paternain

Tensor parameters that are amortized or regularized over large tensor powers, often called "asymptotic" tensor parameters, play a central role in several areas including algebraic complexity theory (constructing fast matrix multiplication…

Computational Complexity · Computer Science 2025-09-11 Jop Briët , Matthias Christandl , Itai Leigh , Amir Shpilka , Jeroen Zuiddam

Matrices of rank at most k are defined by the vanishing of polynomials of degree k + 1 in their entries (namely, their (k + 1)-times-(k + 1)-subdeterminants), regardless of the size of the matrix. We prove a qualitative analogue of this…

Algebraic Geometry · Mathematics 2015-01-14 Jan Draisma , Jochen Kuttler

This paper addresses the detection of a low rank high-dimensional tensor corrupted by an additive complex Gaussian noise. In the asymptotic regime where all the dimensions of the tensor converge towards $+\infty$ at the same rate, existing…

Signal Processing · Electrical Eng. & Systems 2018-02-21 Antoine Chevreuil , Philippe Loubaton

It is well-known that tensor decompositions show separations, that is, that constraints on local terms (such as positivity) may entail an arbitrarily high cost in their representation. Here we show that many of these separations disappear…

Optimization and Control · Mathematics 2021-09-03 Gemma De las Cuevas , Andreas Klingler , Tim Netzer

We prove (without exceptions) the existence of irredundant tensor decompositions with the number of addenda equal to rank $+1$. We also discuss the existence of decompositions with more than the tensor rank terms, which are concise, while…

Algebraic Geometry · Mathematics 2020-02-17 Edoardo Ballico

Let n_d denote the degree d Veronese embedding of a projective space P^r. For any symmetric tensor P, the 'symmetric tensor rank' sr(P) is the minimal cardinality of a subset A of P^r, such that n_d(A) spans P. Let S(P) be the space of all…

Algebraic Geometry · Mathematics 2012-02-15 Edoardo Ballico , Luca Chiantini

This paper discusses the problem of symmetric tensor decomposition on a given variety $X$: decomposing a symmetric tensor into the sum of tensor powers of vectors contained in $X$. In this paper, we first study geometric and algebraic…

Numerical Analysis · Mathematics 2020-03-24 Jiawang Nie , Ke Ye , Lihong Zhi

A recently found (gr-qc/0303036) 2-index, symmetric, trace-free, divergence-free tensor is introduced for arbitrary source-free electromagnetic fields. The tensor can be constructed for any test Maxwell field in Einstein spaces (including…

General Relativity and Quantum Cosmology · Physics 2007-05-23 José M. M. Senovilla

Asymptotic tensor rank is notoriously difficult to determine. Indeed, determining its value for the $2\times 2$ matrix multiplication tensor would determine the matrix multiplication exponent, a long-standing open problem. On the other…

Computational Complexity · Computer Science 2024-11-26 Matthias Christandl , Koen Hoeberechts , Harold Nieuwboer , Péter Vrana , Jeroen Zuiddam

The exponent $\sigma(T)$ of a tensor $T\in\mathbb{F}^d\otimes\mathbb{F}^d\otimes\mathbb{F}^d$ over a field $\mathbb{F}$ captures the base of the exponential growth rate of the tensor rank of $T$ under Kronecker powers. Tensor exponents are…

Computational Complexity · Computer Science 2024-04-10 Petteri Kaski , Mateusz Michałek

We study the problem of finding orthogonal low-rank approximations of symmetric tensors. In the case of matrices, the approximation is a truncated singular value decomposition which is then symmetric. Moreover, for rank-one approximations…

Numerical Analysis · Mathematics 2019-06-18 Oscar Mickelin , Sertac Karaman

Tensor completion is a natural higher-order generalization of matrix completion where the goal is to recover a low-rank tensor from sparse observations of its entries. Existing algorithms are either heuristic without provable guarantees,…

Data Structures and Algorithms · Computer Science 2023-07-14 Allen Liu , Ankur Moitra

It is easily checkable if a given tensor is a B tensor, or a B$_0$ tensor or not. In this paper, we show that a symmetric B tensor can always be decomposed to the sum of a strictly diagonally dominated symmetric M tensor and several…

Spectral Theory · Mathematics 2014-05-15 Liqun Qi , Yisheng Song

We prove the following vanishing theorem. Let M be an irreducible symmetric space of noncompact type whose dimension exceeds 2 and $M\ne SO_0(2,2)/SO(2)\tm SO(2).$ Let E be any vector bundle over M, Then any E-valued $L^2$ harmonic 1-form…

Differential Geometry · Mathematics 2007-05-23 Xusheng Liu

One of the fundamental open problems in the field of tensors is the border Comon's conjecture: given a symmetric tensor $F\in(\mathbb{C}^n)^{\otimes d}$ for $d\geq 3$, its border and symmetric border ranks are equal. In this paper, we prove…

Algebraic Geometry · Mathematics 2024-11-11 Tomasz Mańdziuk , Emanuele Ventura

The title's century-old conjecture is established fpr D=2 and is likely for all D.

General Relativity and Quantum Cosmology · Physics 2019-07-05 S. Deser

A symmetric tensor of small rank decomposes into a configuration of only few vectors. We study the variety of tensors for which this configuration is a unit norm tight frame.

Algebraic Geometry · Mathematics 2015-11-18 Luke Oeding , Elina Robeva , Bernd Sturmfels

Tensor decomposition on big data has attracted significant attention recently. Among the most popular methods is a class of algorithms that leverages compression in order to reduce the size of the tensor and potentially parallelize…

Machine Learning · Computer Science 2018-11-20 Georgios Tsitsikas , Evangelos E. Papalexakis
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