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Matrices can be decomposed via rank-one approximations: the best rank-one approximation is a singular vector pair, and the singular value decomposition writes a matrix as a sum of singular vector pairs. The singular vector tuples of a…

Algebraic Geometry · Mathematics 2025-12-02 Alvaro Ribot , Emil Horobet , Anna Seigal , Ettore Teixeira Turatti

We introduce $t$-Hermitian forms of arbitrary degree $k$, a natural extension of classical degree $k$ Hermitian forms obtained through a synthesis of the tensor transformation law and the $t$-product of third-order tensors. We show that…

Spectral Theory · Mathematics 2026-02-25 Isaac Dobes

We consider here the problem, which is quite classical in Algebraic geometry, of studying the secant varieties of a projective variety $X$. The case we concentrate on is when $X$ is a Veronese variety, a Grassmannian or a Segre variety. Not…

We establish a general computational scheme designed for a systematic computation of characteristic classes of singular complex algebraic varieties that satisfy a Gysin axiom in a transverse setup. This scheme is explicitly geometric and of…

Algebraic Topology · Mathematics 2024-02-21 Markus Banagl , Dominik Wrazidlo

The paper develops elementary linear algebra methods to compute the determinants of the tensor symmetrizations of quadratic and hermitian forms over fields of good characteristic. Explicit results are given for the partitions $(n)$,…

Combinatorics · Mathematics 2024-09-26 Gabriele Nebe

Most currently used tensor regression models for high-dimensional data are based on Tucker decomposition, which has good properties but loses its efficiency in compressing tensors very quickly as the order of tensors increases, say greater…

Methodology · Statistics 2024-03-20 Yuefeng Si , Yingying Zhang , Yuxi Cai , Chunling Liu , Guodong Li

Tensor decomposition of high-dimensional data often struggles to capture semantically or physically meaningful structures, particularly when relying on reconstruction objectives and fixed-rank constraints. We introduce a no-rank tensor…

Machine Learning · Computer Science 2026-03-03 Maryam Bagherian

We prove a unified and general criterion for the uniqueness of critical points of a functional in the presence of constraints such as positivity, boundedness, or fixed mass. Our method relies on convexity properties along suitable paths and…

Analysis of PDEs · Mathematics 2016-07-20 Denis Bonheure , Juraj Földes , Ederson Moreira dos Santos , Alberto Saldaña , Hugo Tavares

Symmetric tensor decomposition is an important problem with applications in several areas for example signal processing, statistics, data analysis and computational neuroscience. It is equivalent to Waring's problem for homogeneous…

Symbolic Computation · Computer Science 2019-09-12 Matías Bender , Jean-Charles Faugère , Ludovic Perret , Elias Tsigaridas

Separability problem, to decide whether a given state is entangled or not, is a fundamental problem in quantum information theory. We propose a powerful and computationally simple separability criterion, which allows us to detect the…

Quantum Physics · Physics 2009-11-13 Cheng-Jie Zhang , Yong-Sheng Zhang , Shun Zhang , Guang-Can Guo

In this paper, we introduce a novel variational framework rooted in algebraic geometry for the analysis of the Hardy $Z$-function. Our primary contribution lies in the definition and exploration of $\Delta_n(\overline{a})$, a newly devised…

Number Theory · Mathematics 2023-10-24 Yochay Jerby

Let $\mathcal C$ be a real plane algebraic curve defined by the resultant of two polynomials (resp. by the discriminant of a polynomial). Geometrically such a curve is the projection of the intersection of the surfaces $P(x,y,z)=Q(x,y,z)=0$…

Computational Geometry · Computer Science 2015-05-26 Rémi Imbach , Guillaume Moroz , Marc Pouget

Coupled tensor decomposition reveals the joint data structure by incorporating priori knowledge that come from the latent coupled factors. The tensor ring (TR) decomposition is invariant under the permutation of tensors with different mode…

Machine Learning · Computer Science 2020-11-10 Huyan Huang , Yipeng Liu , Ce Zhu

We present a reduction algorithm that simultaneously extends Hermite's reduction for rational functions and the Hermite-like reduction for hyperexponential functions. It yields a unique additive decomposition and allows to decide…

Symbolic Computation · Computer Science 2013-01-23 Alin Bostan , Shaoshi Chen , Frédéric Chyzak , Ziming Li , Guoce Xin

There are significant differences between Helmholtz and Hodge's decomposition theorems, but both share a common flavor. This paper is a first step to bring them together. We here produce Helmholtz theorems for differential 1-forms and…

General Mathematics · Mathematics 2014-04-22 Jose G. Vargas

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

We consider the multigraded Hilbert scheme corresponding to the Hilbert function of a finite number of points in general position in a smooth projective complex toric variety. We develop several criteria for a point of that parameter space…

Algebraic Geometry · Mathematics 2023-06-16 Tomasz Mańdziuk

We investigate inverse diffraction problems for penetrable gratings in a piecewise constant medium. In the TE polarization case, it is proved that a binary grating profile together with the refractive index beneath it can be uniquely…

Analysis of PDEs · Mathematics 2021-11-24 Jianli Xiang , Guanghui Hu

Compressed sensing extends from the recovery of sparse vectors from undersampled measurements via efficient algorithms to the recovery of matrices of low rank from incomplete information. Here we consider a further extension to the…

Numerical Analysis · Mathematics 2014-11-04 Holger Rauhut , Reinhold Schneider , Zeljka Stojanac

The Euler Characteristic Transform (ECT) has proven to be a powerful representation, combining geometrical and topological characteristics of shapes and graphs. However, the ECT was hitherto unable to learn task-specific representations. We…

Machine Learning · Computer Science 2024-03-20 Ernst Roell , Bastian Rieck
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