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Related papers: B tensors and tensor complementarity problems

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Recently, Song and Qi extended the concept of P, P_0 and B matrices to P, P_0, B and B_0 tensors, obtained some properties about these tensors, and proposed many questions for further research. In this paper, we answer three questions…

Combinatorics · Mathematics 2014-05-19 Pingzhi Yuan , Lihua You

A new error bound for the linear complementarity problem is given when the involved matrix is a B-matrix. It is shown that this bound is sharper than some previous bounds [C.Q. Li, Y.T. Li. Note on error bounds for linear complementarity…

Numerical Analysis · Mathematics 2016-03-01 Chaoqian Li , Mengting Gan , Shaorong Yang

In this paper, we examine structured tensors which have sum-of-squares (SOS) tensor decomposition, and study the SOS-rank of SOS tensor decomposition. We first show that several classes of even order symmetric structured tensors available…

Spectral Theory · Mathematics 2015-10-13 Haibin Chen , Guoyin Li , Liqun Qi

The complexity of bilinear maps (equivalently, of $3$-mode tensors) has been studied extensively, most notably in the context of matrix multiplication. While circuit complexity and tensor rank coincide asymptotically for $3$-mode tensors,…

Computational Complexity · Computer Science 2026-02-13 Cornelius Brand , Radu Curticapean , Petteri Kaski , Baitian Li , Ian Orzel , Tim Seppelt , Jiaheng Wang

We define new norms for symmetric tensors over ordered normed spaces; these norms are defined by considering linear combinations of tensor products or powers of positive elements only. Relations between the different norms are studied. The…

Functional Analysis · Mathematics 2018-11-07 Svante Janson

Bi-quadratic programming over unit spheres is a fundamental problem in quantum mechanics introduced by pioneer work of Einstein, Schr\"odinger, and others. It has been shown to be NP-hard; so it must be solve by efficient heuristic…

Numerical Analysis · Mathematics 2022-08-23 Shigui Li , Linzhang Lu , Xing Qiu , Zhen Chen , Delu Zeng

This article explores a new type of nonlinear complementarity problem, namely the horizontal tensor complementarity problem (HTCP), which is a natural extension of the horizontal linear complementarity problem studied in [12]. We extend the…

Optimization and Control · Mathematics 2023-12-05 Punit Kumar Yadav , Sonali Sharma , K. Palpandi

The class of MB(MB0)-tensors, which is a generation of B(B0)-tensors and quasi-double B(B0)-tensors, is proposed. And we prove that an even order symmetric MB(MB0)-tensor is positive (semi-)definite. This provides a positive answer for the…

Numerical Analysis · Mathematics 2014-12-02 Chaoqian Li , Yaotang Li

A new $Z$-eigenvalue inclusion theorem for tensors is given and proved to be tighter than those in [G. Wang, G.L. Zhou, L. Caccetta, $Z$-eigenvalue inclusion theorems for tensors, Discrete and Continuous Dynamical Systems Series B,22(1)…

Numerical Analysis · Mathematics 2017-05-16 Jianxing Zhao

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

We establish several mathematical and computational properties of the nuclear norm for higher-order tensors. We show that like tensor rank, tensor nuclear norm is dependent on the choice of base field --- the value of the nuclear norm of a…

Computational Complexity · Computer Science 2016-05-19 Shmuel Friedland , Lek-Heng Lim

We give a spectral algorithm for decomposing overcomplete order-4 tensors, so long as their components satisfy an algebraic non-degeneracy condition that holds for nearly all (all but an algebraic set of measure $0$) tensors over…

Machine Learning · Computer Science 2022-03-08 Samuel B. Hopkins , Tselil Schramm , Jonathan Shi

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

In this paper we study the set of tensors that admit a special type of decomposition called an orthogonal tensor train decomposition. Finding equations defining varieties of low-rank tensors is generally a hard problem, however, the set of…

Algebraic Geometry · Mathematics 2021-11-01 Pardis Semnani , Elina Robeva

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

We give a sufficient criterion for a lower bound of the cactus rank of a tensor. Then we refine that criterion in order to be able to give an explicit sufficient condition for a non-redundant decomposition of a tensor to be minimal and…

Algebraic Geometry · Mathematics 2017-05-08 Edoardo Ballico , Alessandra Bernardi , Luca Chiantini , Elena Guardo

Interest in higher-order tensors has recently surged in data-intensive fields, with a wide range of applications including image processing, blind source separation, community detection, and feature extraction. A common paradigm in…

Numerical Analysis · Mathematics 2020-03-11 Miaoyan Wang , Khanh Dao Duc , Jonathan Fischer , Yun S. Song

In this article, we define new classes of tensors called double $\overline{B}$-tensors, quasi-double $\overline{B}$-tensors and establish some of their properties. Using these properties, we construct new regions viz., double…

Spectral Theory · Mathematics 2015-07-17 Hongwei Jin , M. Rajesh Kannan , Minru Bai

Tensor completion is a technique of filling missing elements of the incomplete data tensors. It being actively studied based on the convex optimization scheme such as nuclear-norm minimization. When given data tensors include some noises,…

Computer Vision and Pattern Recognition · Computer Science 2018-01-11 Tatsuya Yokota , Hidekata Hontani

Tensors are often compressed by expressing them in low rank tensor formats. In this paper, we develop three methodologies that bound the compressibility of a tensor: (1) Algebraic structure, (2) Smoothness, and (3) Displacement structure.…

Numerical Analysis · Mathematics 2020-02-04 Tianyi Shi , Alex Townsend
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