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We introduce the concept of shape partition of a tensor and formulate a general tensor eigenvalue problem that includes all previously studied eigenvalue problems as special cases. We formulate irreducibility and symmetry properties of a…

Spectral Theory · Mathematics 2021-02-25 Antoine Gautier , Francesco Tudisco , Matthias Hein

In this paper, we mainly focus on how to generalize some conclusions from nonnegative irreducible tensors to nonnegative weakly irreducible tensors. To do so, a basic and important lemma is proven using new tools. First, we give the…

Spectral Theory · Mathematics 2012-03-14 Yuning Yang , Qingzhi Yang

We study the l^{p_1,...,p_m} singular value problem for non-negative tensors. We prove a general Perron-Frobenius theorem for weakly irreducible and irreducible nonnegative tensors and provide a Collatz-Wielandt characterization of the…

Spectral Theory · Mathematics 2015-03-05 Antoine Gautier , Matthias Hein

The Perron-Frobenius theorem of nonnegative matrices is a classical result on spectral theory of matrices, which has wide applications in many domains. In this paper, we give the Perron-Frobenius theorem for dual tensors, that is, a dual…

Combinatorics · Mathematics 2025-10-01 Changjiang Bu , Yue Chu , Qingying Zhang , Jiang Zhou

Tubal scalars are usual vectors, and tubal matrices are matrices with every element being a tubal scalar. Such a matrix is often recognized as a third-order tensor. The product between tubal scalars, tubal vectors, and tubal matrices can be…

Spectral Theory · Mathematics 2021-07-28 Yuning Yang , Junwei Zhang

We prove an analog of Perron-Frobenius theorem for multilinear forms with nonnegative coefficients, and more generally, for polynomial maps with nonnegative coefficients. We determine the geometric convergence rate of the power algorithm to…

Spectral Theory · Mathematics 2011-12-30 S. Friedland , S. Gaubert , L. Han

For matrices with all nonnegative entries, the Perron-Frobenius theorem guarantees the existence of an eigenvector with all nonnegative components. We show that the existence of such an eigenvector is also guaranteed for a very different…

Rings and Algebras · Mathematics 2018-08-30 Hunter Swan

We study the combinatorial and algebraic properties of Nonnegative Matrices. Our results are divided into three different categories. 1. We show a quantitative generalization of the 100 year-old Perron-Frobenius theorem, a fundamental…

Combinatorics · Mathematics 2023-01-20 Jenish C. Mehta

We introduce the notion of order-preserving multi-homogeneous mapping which allows to study Perron-Frobenius type theorems and nonnegative tensors in unified fashion. We prove a weak and strong Perron-Frobenius theorem for these maps and…

Spectral Theory · Mathematics 2017-02-13 Antoine Gautier , Francesco Tudisco , Matthias Hein

We study M-tensors and various properties of M-tensors are given. Specially, we show that the smallest real eigenvalue of M-tensor is positive corresponding to a nonnegative eigenvector. We propose an algorithm to find the smallest positive…

Numerical Analysis · Mathematics 2012-03-01 Liping Zhang , Liqun Qi , Guanglu Zhou

An algorithm for finding the eigenvalue of a nonnegative irreducible tensor was recently proposed by Michael Ng, Liqun Qi, and Guanglu Zhou in {\it Finding the largest eigenvalue of a nonnegative tensor}. However, the authors did not prove…

Numerical Analysis · Mathematics 2010-11-19 K. J. Pearson

In this paper, we introduce a new class of nonnegative tensors --- strictly nonnegative tensors. A weakly irreducible nonnegative tensor is a strictly nonnegative tensor but not vice versa. We show that the spectral radius of a strictly…

Numerical Analysis · Mathematics 2015-03-13 Shenglong Hu , Zheng-Hai Huang , Liqun Qi

We study the decomposition of a nonnegative tensor into a minimal sum of outer product of nonnegative vectors and the associated parsimonious naive Bayes probabilistic model. We show that the corresponding approximation problem, which is…

Numerical Analysis · Computer Science 2009-04-12 Lek-Heng Lim , Pierre Comon

We propose a modified Newton iteration for finding some nonnegative Z-eigenpairs of a nonnegative tensor. When the tensor is irreducible, all nonnegative eigenpairs are known to be positive. We prove local quadratic convergence of the new…

Numerical Analysis · Mathematics 2017-05-23 Chun-Hua Guo , Wen-Wei Lin , Ching-Sung Liu

We propose a theory of eigenvalues, eigenvectors, singular values, and singular vectors for tensors based on a constrained variational approach much like the Rayleigh quotient for symmetric matrix eigenvalues. These notions are particularly…

Spectral Theory · Mathematics 2007-05-23 Lek-Heng Lim

In this article we prove the strict monotonicity of the spectral radius of weakly irreducible nonnegative tensors. As an application, we give a necessary and sufficient condition for an interval hull of tensors to be contained in the set of…

Spectral Theory · Mathematics 2021-05-11 M. Rajesh Kannan , Naomi Shaked-Monderer , Abraham Berman

The concept of double nonnegativity of matrices is generalized to doubly nonnegative tensors by means of the nonnegativity of all entries and $H$-eigenvalues. This generalization is defined for tensors of any order (even or odd), while it…

Spectral Theory · Mathematics 2015-06-10 Ziyan Luo , Liqun Qi

Generalized inverses of tensors play increasingly important roles in computational mathematics and numerical analysis. It is appropriate to develop the theory of generalized inverses of tensors within the algebraic structure of a ring. In…

Rings and Algebras · Mathematics 2021-09-24 Ratikanta Behera , Jajati Keshari Sahoo , R. N. Mohapatra , M. Zuhair Nashed

Power nonnegative matrices are defined as complex matrices having at least one nonnegative integer power. We exploit the possibility of deriving a Perron Frobenius-like theory for these matrices, obtaining three main results and drawing…

Numerical Analysis · Mathematics 2013-11-21 F. Tudisco , V. Cardinali , C. Di Fiore

Nonparametric extension of tensor regression is proposed. Nonlinearity in a high-dimensional tensor space is broken into simple local functions by incorporating low-rank tensor decomposition. Compared to naive nonparametric approaches, our…

Machine Learning · Statistics 2016-03-09 Masaaki Imaizumi , Kohei Hayashi
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