Matrix Equations and Symmetry

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (29 April 2022) | Viewed by 3552

Special Issue Editors


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Guest Editor
Leipzig University of Applied Sciences
Interests: numerical and applied multilinear algebra (methods for eigenvalue problems, linear systems, matrix equations and matrix functions, preconditioning, tensor methods); model order reduction; mathematical systems and control theory; systems of polynomials

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Guest Editor
University of Hamburg Technische Universität Berlin
Interests: differential-algebraic equations; optimal and robust control; matrix equations and inequalities;numerical linear algebra (in particular, structured and nonlinear eigenvalue problems); numerical optimization, model order reduction;mathematical software; mathematics applications in engineering (mechanics, vibroacoustics)

Special Issue Information

Dear Colleagues,

Algebraic and differential matrix equations arise in many different applications areas, e.g., in control theory, model order reduction, uncertainty quantification, signal processing, discretizations of deterministic and stochastic PDEs, and matrix regression problems, to name only a few. In recent decades, there has been a strong and steadily increasing research interest in this topic which has led to many new substantial results regarding both theoretical insights and numerical solution strategies. For instance, various recent developments using low-rank matrix approximations have paved the way for handling high-dimensional matrix equations. Symmetry arises in the context of matrix equations in different forms, such as equations defined by symmetric coefficient matrices, equations allowing symmetric solutions or solution structures. Hence, the need arises to exploit these symmetric structures in both theoretical analysis and numerical methods.

The MPDI journal Symmetry is planning to set up a Special Issue on “Matrix Equations and Symmetry”. With this Special Issue, we aim at collecting high-quality papers which highlight new developments, theoretical and numerical, for matrix equations with a particular emphasis on symmetric structures arising in this context. Papers showcasing emerging new application areas for matrix equations are also welcome.

Dr. Patrick Kürschner
Dr. Matthias Voigt
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Algebraic matrix equations
  • Differential matrix equations
  • Numerical algorithms
  • Symmetry exploitation
  • Symmetric matrices
  • Large-scale problems
  • Low-rank approximations
  • Iterative methods

Published Papers (2 papers)

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Research

8 pages, 247 KiB  
Article
Nonnegative Estimation of Variance Components for a Nested Three-Way Random Model
by Jaesung Choi
Symmetry 2022, 14(6), 1210; https://doi.org/10.3390/sym14061210 - 11 Jun 2022
Viewed by 1266
Abstract
A nonnegative variance estimation procedure is suggested for an unbalanced data where two factors are nested in another. Since the involved factors are all random, the approach is based on a nested three-way random model. The proposed method for the estimation of variance [...] Read more.
A nonnegative variance estimation procedure is suggested for an unbalanced data where two factors are nested in another. Since the involved factors are all random, the approach is based on a nested three-way random model. The proposed method for the estimation of variance components is compared with Henderson’s Method I and III in view of the same estimation procedure based on the method of moments. Although both the Henderson’s Method I and III are known to be useful for the estimation of variance components for balanced or unbalanced data, they often yield negative values as variance estimates whereas the estimates by the suggested method are never be negative. Hence, it points out what makes this happen and discusses how to fix the problem. The proposed method shows how to define sums of squares and the orthogonal coefficient matrices that are necessary for the evaluation of expectations. All the matrices of the quadratic forms for computing sums of squares are symmetric and idempotent. It also reveals the individual coefficient of each variance component does not change from equation to equation. Full article
(This article belongs to the Special Issue Matrix Equations and Symmetry)
12 pages, 283 KiB  
Article
Solving the Sylvester-Transpose Matrix Equation under the Semi-Tensor Product
by Janthip Jaiprasert and Pattrawut Chansangiam
Symmetry 2022, 14(6), 1094; https://doi.org/10.3390/sym14061094 - 26 May 2022
Cited by 3 | Viewed by 1323
Abstract
This paper investigates the Sylvester-transpose matrix equation AX+XTB=C, where all mentioned matrices are over an arbitrary field. Here, ⋉ is the semi-tensor product, which is a generalization of the usual matrix product defined [...] Read more.
This paper investigates the Sylvester-transpose matrix equation AX+XTB=C, where all mentioned matrices are over an arbitrary field. Here, ⋉ is the semi-tensor product, which is a generalization of the usual matrix product defined for matrices of arbitrary dimensions. For matrices of compatible dimensions, we investigate criteria for the equation to have a solution, a unique solution, or infinitely many solutions. These conditions rely on ranks and linear dependence. Moreover, we find suitable matrix partitions so that the matrix equation can be transformed into a linear system involving the usual matrix product. Our work includes the studies of the equation AX=C, the equation XB=C, and the classical Sylvester-transpose matrix equation. Full article
(This article belongs to the Special Issue Matrix Equations and Symmetry)
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