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For finding the numerical solution of operator equations in many applications a decomposition in subspaces is needed. Therefore, it is necessary to extend the known method of matrix representation to the utilization of fusion frames. In…

Functional Analysis · Mathematics 2020-07-14 Peter Balazs , Mitra Shamsabadi , Ali Akbar Arefijamaal , Chilles Gardon

In this work a possibility of a decomposition of a bounded operator which acts in a Hilbert space $H$ as a product of a J-unitary and a J-self-adjoint operators is studied, $J$ is a conjugation (an antilinear involution). Decompositions of…

Functional Analysis · Mathematics 2009-10-15 Sergey M. Zagorodnyuk

Multivariate polynomials arise in many different disciplines. Representing such a polynomial as a vector of univariate polynomials can offer useful insight, as well as more intuitive understanding. For this, techniques based on tensor…

Optimization and Control · Mathematics 2016-01-29 Gabriel Hollander , Philippe Dreesen , Mariya Ishteva , Johan Schoukens

This article describes the theoretical foundation of and explicit algorithms for a novel approach to morphology and anisotropy analysis of complex spatial structure using tensor-valued Minkowski functionals, the so-called Minkowski tensors.…

The universality properties of kernels characterize the class of functions that can be approximated in the associated reproducing kernel Hilbert space and are of fundamental importance in the theoretical underpinning of kernel methods in…

Machine Learning · Computer Science 2025-06-25 Franziskus Steinert , Salem Said , Cyrus Mostajeran

We prove a generalization to Jennrich's uniqueness theorem for tensor decompositions in the undercomplete setting. Our uniqueness theorem is based on an alternative definition of the standard tensor decomposition, which we call…

Computational Complexity · Computer Science 2025-10-31 Pascal Koiran , Rafael Oliveira

It is shown how one can apply the classification of the holonomy algebras of Lorentzian manifolds to solve some problems. In particular, a new proof to the classification of Lorentzian manifolds with recurrent curvature tensor is given; the…

Differential Geometry · Mathematics 2018-08-21 Anton S. Galaev

This paper presents a canonical polyadic (CP) tensor decomposition that addresses unaligned observations. The mode with unaligned observations is represented using functions in a reproducing kernel Hilbert space (RKHS). We introduce a…

Machine Learning · Statistics 2025-08-12 Runshi Tang , Tamara Kolda , Anru R. Zhang

The second order Killing and conformal tensors are analyzed in terms of their spectral decomposition, and some properties of the eigenvalues and the eigenspaces are shown. When the tensor is of type I with only two different eigenvalues,…

General Relativity and Quantum Cosmology · Physics 2009-11-11 Bartolomé Coll , Joan Josep Ferrando , Juan Antonio Sáez

In this paper we develope a Morsification Theory for holomorphic functions defining a singularity of finite codimension with respect to an ideal, which recovers most previously known Morsification results for non-isolated singulatities and…

Algebraic Geometry · Mathematics 2007-05-23 Javier Fernandez de Bobadilla

Differential geometries derived from tensor decompositions have been extensively studied and provided the foundations for a variety of efficient numerical methods. Despite the practical success of the tensor ring (TR) decomposition, its…

Numerical Analysis · Mathematics 2026-01-30 Bin Gao , Renfeng Peng , Ya-xiang Yuan

We present the first deterministic, finite-step algorithm for exact tensor ring (TR) decomposition, addressing an open question about the existence of such procedures. Our method leverages blockwise simultaneous diagonalization to recover…

Numerical Analysis · Mathematics 2025-12-02 Han Chen , Sitan Chen , Anru R. Zhang

We use a degeneration of the 1D double affine Hecke algebra and the Dunkl operator to study systematically nonsymmetric Bessel functions and their truncations.

Quantum Algebra · Mathematics 2007-05-23 Ivan Cherednik , Yavor Markov

Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions. While tensors first emerged in the psychometrics community in the $20^{\text{th}}$ century, they have since then spread to…

Machine Learning · Statistics 2017-11-30 Stephan Rabanser , Oleksandr Shchur , Stephan Günnemann

The problem of classification of decomposable (in the sense of Stormer) positive maps between matrix algebras is presented. We propose the new notion of "finite" version of decomposability ($k$-decomposabilty). The characterisation of…

Quantum Physics · Physics 2007-05-23 Wladyslaw A. Majewski , Marcin Marciniak

In many instances one has to deal with parametric models. Such models in vector spaces are connected to a linear map. The reproducing kernel Hilbert space and affine- / linear- representations in terms of tensor products are directly…

Numerical Analysis · Mathematics 2018-11-26 Hermann G. Matthies , Roger Ohayon

We present an approach to defining Hilbert spaces of functions depending on infinitely many variables or parameters, with emphasis on a weighted tensor product construction based on stable space splittings, The construction has been used in…

Numerical Analysis · Mathematics 2016-07-21 Michael Griebel , Peter Oswald

Given a countable metric space, we can consider its end. Then a basis of a Hilbert space indexed by the metric space defines an end of the Hilbert space, which is a new notion and different from an end as a metric space. Such an indexed…

K-Theory and Homology · Mathematics 2021-08-25 Tsuyoshi Kato , Daisuke Kishimoto , Mitsunobu Tsutaya

To study a one parameter deformation of an $S_1$ singularity taking into consideration its differential geometric properties, we give a form representing the deformation using only diffeomorphisms on the source and isometries of the target.…

Differential Geometry · Mathematics 2025-05-13 Runa Shimada

Tensors are a fundamental data structure for many scientific contexts, such as time series analysis, materials science, and physics, among many others. Improving our ability to produce and handle tensors is essential to efficiently address…

Machine Learning · Statistics 2026-02-12 Wilson G. Gregory , Josué Tonelli-Cueto , Nicholas F. Marshall , Andrew S. Lee , Soledad Villar