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Conjugate partial-symmetric (CPS) tensor is a generalization of Hermitian matrices. For the CPS tensor decomposition some properties are presented. For real CPS tensors in particular, we note the subtle difference from the complex case of…

Numerical Analysis · Mathematics 2021-11-08 Pengfei Huang , Qingzhi Yang

In this article we consider the sparse solutions of the tensor complementarity problem (TCP) which are the solutions of the smallest cardinality. We establish a connection between the least element of the feasible solution set of TCP and…

Optimization and Control · Mathematics 2023-04-25 R. Deb , A. K. Das

This paper studies nuclear norms of symmetric tensors. As recently shown by Friedland and Lim, the nuclear norm of a symmetric tensor can be achieved at a symmetric decomposition. We discuss how to compute symmetric tensor nuclear norms,…

Optimization and Control · Mathematics 2016-05-31 Jiawang Nie

One of the most challenging problems in quantum physics is to quantify the entanglement of $d$-partite states and their separability. We show here that these problems are best addressed using tensors. The geometric measure of entanglement…

Quantum Physics · Physics 2025-11-05 Shmuel Friedland

In particle physics, scalar potentials have to be bounded from below in order for the physics to make sense. The precise expressions of checking lower bound of scalar potentials are essential, which is an analytical expression of checking…

Optimization and Control · Mathematics 2022-02-09 Yisheng Song , Liqun Qi

The question of how to certify the non-negativity of a polynomial function lies at the heart of Real Algebra and it also has important applications to Optimization. In the setting of symmetric polynomials Timofte provided a useful way of…

Optimization and Control · Mathematics 2015-10-21 Cordian Riener

Low rank tensor decompositions are a powerful tool for learning generative models, and uniqueness results give them a significant advantage over matrix decomposition methods. However, tensors pose significant algorithmic challenges and…

Data Structures and Algorithms · Computer Science 2014-01-21 Aditya Bhaskara , Moses Charikar , Ankur Moitra , Aravindan Vijayaraghavan

Real eigenpairs of symmetric tensors play an important role in multiple applications. In this paper we propose and analyze a fast iterative Newton-based method to compute real eigenpairs of symmetric tensors. We derive sufficient conditions…

Numerical Analysis · Mathematics 2018-03-06 Ariel Jaffe , Roi Weiss , Boaz Nadler

The Tensor Isomorphism problem (TI) has recently emerged as having connections to multiple areas of research within complexity and beyond, but the current best upper bound is essentially the brute force algorithm. Being an algebraic…

Computational Complexity · Computer Science 2023-06-01 Nicola Galesi , Joshua A. Grochow , Toniann Pitassi , Adrian She

Let $(X,\Delta)$ be a smooth complex projective simple normal crossing pair of dimension $n\geq 3$ endowed with an everywhere nondegenerate logarithmic conformal tensor. If $K_X+\Delta$ is not nef, then precisely one of the following…

Algebraic Geometry · Mathematics 2026-04-20 Maurício Corrêa , Alex Massarenti

We continue the study of isomorphisms of tensor algebras associated to a C*-correspondences in the sense of Muhly and Solel. Inspired by by recent work of Davidson, Ramsey and Shalit, we solve isomorphism problems for tensor algebras…

Operator Algebras · Mathematics 2016-08-16 Adam Dor-On

Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications…

Machine Learning · Statistics 2018-05-04 Qingquan Song , Hancheng Ge , James Caverlee , Xia Hu

This paper proposes a novel formulation of the tensor completion problem to impute missing entries of data represented by tensors. The formulation is introduced in terms of tensor train (TT) rank which can effectively capture global…

Numerical Analysis · Computer Science 2016-01-07 Ho N. Phien , Hoang D. Tuan , Johann A. Bengua , Minh N. Do

In this paper, we propose two new classes of tensors: double B-tensors and quasi-double B-tensors, give some properties of double B-tensors and quasi-double B-tensors, discuss their relationships with B-tensors and positive definite tensors…

Rings and Algebras · Mathematics 2014-08-13 Chaoqian Li , Yaotang Li

This chapter studies the problem of decomposing a tensor into a sum of constituent rank one tensors. While tensor decompositions are very useful in designing learning algorithms and data analysis, they are NP-hard in the worst-case. We will…

Data Structures and Algorithms · Computer Science 2020-07-31 Aravindan Vijayaraghavan

An exact solution has an axial symmetry is obtained in the teleparallel theory of gravitation. The associated metric has the structure function G(xi)=1-xi^2-2mA(xi)^3. The cubic nature of the structure function can make calculations…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Gamal G. L. Nashed

A novel tensor-based formula for solving the linear systems involving Kronecker sum is proposed. Such systems are directly related to the matrix and tensor forms of Sylvester equation. The new tensor-based formula demonstrates the…

General Mathematics · Mathematics 2025-04-15 Ahmad Y. Al-Dweik , Abdallah Sayyed-Ahmad

It is proved that if there exists a positive and continuous function $f$ on an $n$-dimensional complex manifold $X$, $q$-convex with corners outside a compact set $K\subset X$ and which exhausts $X$ from below, then…

Complex Variables · Mathematics 2025-10-09 Youssef Alaoui

We show that a best rank one approximation to a real symmetric tensor, which in principle can be nonsymmetric, can be chosen symmetric. Furthermore, a symmetric best rank one approximation to a symmetric tensor is unique if the tensor does…

Functional Analysis · Mathematics 2012-11-27 Shmuel Friedland

In this paper, we study the sparse nonnegative tensor factorization and completion problem from partial and noisy observations for third-order tensors. Because of sparsity and nonnegativity, the underlying tensor is decomposed into the…

Machine Learning · Statistics 2021-10-22 Xiongjun Zhang , Michael K. Ng