Highly Symmetric Generalized Circulant Permutation Matricesୋ
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Problems in Abstract Algebra
STUDENT MATHEMATICAL LIBRARY Volume 82 Problems in Abstract Algebra A. R. Wadsworth 10.1090/stml/082 STUDENT MATHEMATICAL LIBRARY Volume 82 Problems in Abstract Algebra A. R. Wadsworth American Mathematical Society Providence, Rhode Island Editorial Board Satyan L. Devadoss John Stillwell (Chair) Erica Flapan Serge Tabachnikov 2010 Mathematics Subject Classification. Primary 00A07, 12-01, 13-01, 15-01, 20-01. For additional information and updates on this book, visit www.ams.org/bookpages/stml-82 Library of Congress Cataloging-in-Publication Data Names: Wadsworth, Adrian R., 1947– Title: Problems in abstract algebra / A. R. Wadsworth. Description: Providence, Rhode Island: American Mathematical Society, [2017] | Series: Student mathematical library; volume 82 | Includes bibliographical references and index. Identifiers: LCCN 2016057500 | ISBN 9781470435837 (alk. paper) Subjects: LCSH: Algebra, Abstract – Textbooks. | AMS: General – General and miscellaneous specific topics – Problem books. msc | Field theory and polyno- mials – Instructional exposition (textbooks, tutorial papers, etc.). msc | Com- mutative algebra – Instructional exposition (textbooks, tutorial papers, etc.). msc | Linear and multilinear algebra; matrix theory – Instructional exposition (textbooks, tutorial papers, etc.). msc | Group theory and generalizations – Instructional exposition (textbooks, tutorial papers, etc.). msc Classification: LCC QA162 .W33 2017 | DDC 512/.02–dc23 LC record available at https://lccn.loc.gov/2016057500 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy select pages for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. -
A Matrix Handbook for Statisticians
A MATRIX HANDBOOK FOR STATISTICIANS George A. F. Seber Department of Statistics University of Auckland Auckland, New Zealand BICENTENNIAL BICENTENNIAL WILEY-INTERSCIENCE A John Wiley & Sons, Inc., Publication This Page Intentionally Left Blank A MATRIX HANDBOOK FOR STATISTICIANS THE WlLEY BICENTENNIAL-KNOWLEDGE FOR GENERATIONS Gachgeneration has its unique needs and aspirations. When Charles Wiley first opened his small printing shop in lower Manhattan in 1807, it was a generation of boundless potential searching for an identity. And we were there, helping to define a new American literary tradition. Over half a century later, in the midst of the Second Industrial Revolution, it was a generation focused on building the future. Once again, we were there, supplying the critical scientific, technical, and engineering knowledge that helped frame the world. Throughout the 20th Century, and into the new millennium, nations began to reach out beyond their own borders and a new international community was born. Wiley was there, expanding its operations around the world to enable a global exchange of ideas, opinions, and know-how. For 200 years, Wiley has been an integral part of each generation's journey, enabling the flow of information and understanding necessary to meet their needs and fulfill their aspirations. Today, bold new technologies are changing the way we live and learn. Wiley will be there, providing you the must-have knowledge you need to imagine new worlds, new possibilities, and new opportunities. Generations come and go, but you can always count on Wiley to provide you the knowledge you need, when and where you need it! n WILLIAM J. -
Group Matrix Ring Codes and Constructions of Self-Dual Codes
Group Matrix Ring Codes and Constructions of Self-Dual Codes S. T. Dougherty University of Scranton Scranton, PA, 18518, USA Adrian Korban Department of Mathematical and Physical Sciences University of Chester Thornton Science Park, Pool Ln, Chester CH2 4NU, England Serap S¸ahinkaya Tarsus University, Faculty of Engineering Department of Natural and Mathematical Sciences Mersin, Turkey Deniz Ustun Tarsus University, Faculty of Engineering Department of Computer Engineering Mersin, Turkey February 2, 2021 arXiv:2102.00475v1 [cs.IT] 31 Jan 2021 Abstract In this work, we study codes generated by elements that come from group matrix rings. We present a matrix construction which we use to generate codes in two different ambient spaces: the matrix ring Mk(R) and the ring R, where R is the commutative Frobenius ring. We show that codes over the ring Mk(R) are one sided ideals in the group matrix k ring Mk(R)G and the corresponding codes over the ring R are G - codes of length kn. Additionally, we give a generator matrix for self- dual codes, which consist of the mentioned above matrix construction. 1 We employ this generator matrix to search for binary self-dual codes with parameters [72, 36, 12] and find new singly-even and doubly-even codes of this type. In particular, we construct 16 new Type I and 4 new Type II binary [72, 36, 12] self-dual codes. 1 Introduction Self-dual codes are one of the most widely studied and interesting class of codes. They have been shown to have strong connections to unimodular lattices, invariant theory, and designs. -
Characterization and Properties of .R; S /-Commutative Matrices
Characterization and properties of .R;S/-commutative matrices William F. Trench Trinity University, San Antonio, Texas 78212-7200, USA Mailing address: 659 Hopkinton Road, Hopkinton, NH 03229 USA NOTICE: this is the author’s version of a work that was accepted for publication in Linear Algebra and Its Applications. Changes resulting from the publishing process, such as peer review, editing,corrections, structural formatting,and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Please cite this article in press as: W.F. Trench, Characterization and properties of .R;S /-commutative matrices, Linear Algebra Appl. (2012), doi:10.1016/j.laa.2012.02.003 Abstract 1 Cmm Let R D P diag. 0Im0 ; 1Im1 ;:::; k1Imk1 /P 2 and S D 1 Cnn Q diag. .0/In0 ; .1/In1 ;:::; .k1/Ink1 /Q 2 , where m0 C m1 C C mk1 D m, n0 C n1 CC nk1 D n, 0, 1, ..., k1 are distinct mn complex numbers, and W Zk ! Zk D f0;1;:::;k 1g. We say that A 2 C is .R;S /-commutative if RA D AS . We characterize the class of .R;S /- commutative matrrices and extend results obtained previously for the case where 2i`=k ` D e and .`/ D ˛` C .mod k/, 0 Ä ` Ä k 1, with ˛, 2 Zk. Our results are independent of 0, 1,..., k1, so long as they are distinct; i.e., if RA D AS for some choice of 0, 1,..., k1 (all distinct), then RA D AS for arbitrary of 0, 1,..., k1. -
Some Combinatorial Properties of Centrosymmetric Matrices* Allan B
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Some Combinatorial Properties of Centrosymmetric Matrices* Allan B. G-use Mathematics Department University of San Francisco San Francisco, California 94117 Submitted by Richard A. Bruaidi ABSTRACT The elements in the group of centrosymmetric fl X n permutation matrices are the extreme points of a convex subset of n2-dimensional Euclidean space, which we characterize by a very simple set of linear inequalities, thereby providing an interest- ing solvable case of a difficult problem posed by L. Mirsky, as well as a new analogue of the famous theorem on doubly stochastic matrices due to G. Bid&off. Several further theorems of a related nature also are included. INTRODUCTION A square matrix of non-negative real numbers is doubly stochastic if the sum of its entries in any row or column is equal to 1. The set 52, of doubly stochastic matrices of size n X n forms a bounded convex polyhedron in n2-dimensional Euclidean space which, according to a classic result of G. Birkhoff [l], has the n X n permutation matrices as its only vertices. L. Mirsky, in his interesting survey paper on the theory of doubly stochastic matrices [2], suggests the problem of developing refinements of the Birkhoff theorem along the following lines: Given a specified subgroup G of the full symmetric group %,, of the n! permutation matrices, determine the poly- hedron lying within 3, whose vertices are precisely the elements of G. In general, as Mirsky notes, this problem appears to be very difficult. -
Circulant and Toeplitz Matrices in Compressed Sensing
Circulant and Toeplitz Matrices in Compressed Sensing Holger Rauhut Hausdorff Center for Mathematics & Institute for Numerical Simulation University of Bonn Conference on Time-Frequency Strobl, June 15, 2009 Institute for Numerical Simulation Rheinische Friedrich-Wilhelms-Universität Bonn Holger Rauhut Hausdorff Center for Mathematics & Institute for NumericalCirculant and Simulation Toeplitz University Matrices inof CompressedBonn Sensing Overview • Compressive Sensing (compressive sampling) • Partial Random Circulant and Toeplitz Matrices • Recovery result for `1-minimization • Numerical experiments • Proof sketch (Non-commutative Khintchine inequality) Holger Rauhut Circulant and Toeplitz Matrices 2 Recovery requires the solution of the underdetermined linear system Ax = y: Idea: Sparsity allows recovery of the correct x. Suitable matrices A allow the use of efficient algorithms. Compressive Sensing N Recover a large vector x 2 C from a small number of linear n×N measurements y = Ax with A 2 C a suitable measurement matrix. Usual assumption x is sparse, that is, kxk0 := j supp xj ≤ k where supp x = fj; xj 6= 0g. Interesting case k < n << N. Holger Rauhut Circulant and Toeplitz Matrices 3 Compressive Sensing N Recover a large vector x 2 C from a small number of linear n×N measurements y = Ax with A 2 C a suitable measurement matrix. Usual assumption x is sparse, that is, kxk0 := j supp xj ≤ k where supp x = fj; xj 6= 0g. Interesting case k < n << N. Recovery requires the solution of the underdetermined linear system Ax = y: Idea: Sparsity allows recovery of the correct x. Suitable matrices A allow the use of efficient algorithms. Holger Rauhut Circulant and Toeplitz Matrices 3 n×N For suitable matrices A 2 C , `0 recovers every k-sparse x provided n ≥ 2k. -
Analytical Solution of the Symmetric Circulant Tridiagonal Linear System
Rose-Hulman Institute of Technology Rose-Hulman Scholar Mathematical Sciences Technical Reports (MSTR) Mathematics 8-24-2014 Analytical Solution of the Symmetric Circulant Tridiagonal Linear System Sean A. Broughton Rose-Hulman Institute of Technology, [email protected] Jeffery J. Leader Rose-Hulman Institute of Technology, [email protected] Follow this and additional works at: https://scholar.rose-hulman.edu/math_mstr Part of the Numerical Analysis and Computation Commons Recommended Citation Broughton, Sean A. and Leader, Jeffery J., "Analytical Solution of the Symmetric Circulant Tridiagonal Linear System" (2014). Mathematical Sciences Technical Reports (MSTR). 103. https://scholar.rose-hulman.edu/math_mstr/103 This Article is brought to you for free and open access by the Mathematics at Rose-Hulman Scholar. It has been accepted for inclusion in Mathematical Sciences Technical Reports (MSTR) by an authorized administrator of Rose-Hulman Scholar. For more information, please contact [email protected]. Analytical Solution of the Symmetric Circulant Tridiagonal Linear System S. Allen Broughton and Jeffery J. Leader Mathematical Sciences Technical Report Series MSTR 14-02 August 24, 2014 Department of Mathematics Rose-Hulman Institute of Technology http://www.rose-hulman.edu/math.aspx Fax (812)-877-8333 Phone (812)-877-8193 Analytical Solution of the Symmetric Circulant Tridiagonal Linear System S. Allen Broughton Rose-Hulman Institute of Technology Jeffery J. Leader Rose-Hulman Institute of Technology August 24, 2014 Abstract A circulant tridiagonal system is a special type of Toeplitz system that appears in a variety of problems in scientific computation. In this paper we give a formula for the inverse of a symmetric circulant tridiagonal matrix as a product of a circulant matrix and its transpose, and discuss the utility of this approach for solving the associated system. -
On Some Properties of Positive Definite Toeplitz Matrices and Their Possible Applications
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector On Some Properties of Positive Definite Toeplitz Matrices and Their Possible Applications Bishwa Nath Mukhexjee Computer Science Unit lndian Statistical Institute Calcutta 700 035, Zndia and Sadhan Samar Maiti Department of Statistics Kalyani University Kalyani, Nadia, lndia Submitted by Miroslav Fiedler ABSTRACT Various properties of a real symmetric Toeplitz matrix Z,,, with elements u+ = u,~_~,, 1 Q j, k c m, are reviewed here. Matrices of this kind often arise in applica- tions in statistics, econometrics, psychometrics, structural engineering, multichannel filtering, reflection seismology, etc., and it is desirable to have techniques which exploit their special structure. Possible applications of the results related to their inverse, determinant, and eigenvalue problem are suggested. 1. INTRODUCTION Most matrix forms that arise in the analysis of stationary stochastic processes, as in time series data, are of the so-called Toeplitz structure. The Toeplitz matrix, T, is important due to its distinctive property that the entries in the matrix depend only on the differences of the indices, and as a result, the elements on its principal diagonal as well as those lying within each of its subdiagonals are identical. In 1910, 0. Toeplitz studied various forms of the matrices ((t,_,)) in relation to Laurent power series and called them Lforms, without assuming that they are symmetric. A sufficiently well-structured theory on T-matrix LINEAR ALGEBRA AND ITS APPLICATIONS 102:211-240 (1988) 211 0 Elsevier Science Publishing Co., Inc., 1988 52 Vanderbilt Ave., New York, NY 10017 00243795/88/$3.50 212 BISHWA NATH MUKHERJEE AND SADHAN SAMAR MAITI also existed in the memoirs of Frobenius [21, 221. -
New Classes of Matrix Decompositions
NEW CLASSES OF MATRIX DECOMPOSITIONS KE YE Abstract. The idea of decomposing a matrix into a product of structured matrices such as tri- angular, orthogonal, diagonal matrices is a milestone of numerical computations. In this paper, we describe six new classes of matrix decompositions, extending our work in [8]. We prove that every n × n matrix is a product of finitely many bidiagonal, skew symmetric (when n is even), companion matrices and generalized Vandermonde matrices, respectively. We also prove that a generic n × n centrosymmetric matrix is a product of finitely many symmetric Toeplitz (resp. persymmetric Hankel) matrices. We determine an upper bound of the number of structured matrices needed to decompose a matrix for each case. 1. Introduction Matrix decomposition is an important technique in numerical computations. For example, we have classical matrix decompositions: (1) LU: a generic matrix can be decomposed as a product of an upper triangular matrix and a lower triangular matrix. (2) QR: every matrix can be decomposed as a product of an orthogonal matrix and an upper triangular matrix. (3) SVD: every matrix can be decomposed as a product of two orthogonal matrices and a diagonal matrix. These matrix decompositions play a central role in engineering and scientific problems related to matrix computations [1],[6]. For example, to solve a linear system Ax = b where A is an n×n matrix and b is a column vector of length n. We can first apply LU decomposition to A to obtain LUx = b, where L is a lower triangular matrix and U is an upper triangular matrix. -
Discovering Transforms: a Tutorial on Circulant Matrices, Circular Convolution, and the Discrete Fourier Transform
DISCOVERING TRANSFORMS: A TUTORIAL ON CIRCULANT MATRICES, CIRCULAR CONVOLUTION, AND THE DISCRETE FOURIER TRANSFORM BASSAM BAMIEH∗ Key words. Discrete Fourier Transform, Circulant Matrix, Circular Convolution, Simultaneous Diagonalization of Matrices, Group Representations AMS subject classifications. 42-01,15-01, 42A85, 15A18, 15A27 Abstract. How could the Fourier and other transforms be naturally discovered if one didn't know how to postulate them? In the case of the Discrete Fourier Transform (DFT), we show how it arises naturally out of analysis of circulant matrices. In particular, the DFT can be derived as the change of basis that simultaneously diagonalizes all circulant matrices. In this way, the DFT arises naturally from a linear algebra question about a set of matrices. Rather than thinking of the DFT as a signal transform, it is more natural to think of it as a single change of basis that renders an entire set of mutually-commuting matrices into simple, diagonal forms. The DFT can then be \discovered" by solving the eigenvalue/eigenvector problem for a special element in that set. A brief outline is given of how this line of thinking can be generalized to families of linear operators, leading to the discovery of the other common Fourier-type transforms, as well as its connections with group representations theory. 1. Introduction. The Fourier transform in all its forms is ubiquitous. Its many useful properties are introduced early on in Mathematics, Science and Engineering curricula [1]. Typically, it is introduced as a transformation on functions or signals, and then its many useful properties are easily derived. Those properties are then shown to be remarkably effective in solving certain differential equations, or in an- alyzing the action of time-invariant linear dynamical systems, amongst many other uses. -
Packing of Permutations Into Latin Squares 10
PACKING OF PERMUTATIONS INTO LATIN SQUARES STEPHAN FOLDES, ANDRAS´ KASZANYITZKY, AND LASZL´ O´ MAJOR Abstract. For every positive integer n greater than 4 there is a set of Latin squares of order n such that every permutation of the numbers 1,...,n appears exactly once as a row, a column, a reverse row or a reverse column of one of the given Latin squares. If n is greater than 4 and not of the form p or 2p for some prime number p congruent to 3 modulo 4, then there always exists a Latin square of order n in which the rows, columns, reverse rows and reverse columns are all distinct permutations of 1,...,n, and which constitute a permutation group of order 4n. If n is prime congruent to 1 modulo 4, then a set of (n − 1)/4 mutually orthogonal Latin squares of order n can also be constructed by a classical method of linear algebra in such a way, that the rows, columns, reverse rows and reverse columns are all distinct and constitute a permutation group of order n(n − 1). 1. Introduction A permutation is a bijective map f from a finite, n-element set (the domain of the permutation) to itself. When the domain is fixed, or it is an arbitrary n-element set, the group of all permutations on that domain is denoted by Sn (full symmetric group). If the elements of the domain are enumerated in some well defined order as z1,...,zn, then the sequence f(z1),...,f(zn) is called the sequence representation of the permutation f. -
Linear Algebra: Example Sheet 3 of 4
Michaelmas Term 2020 Linear Algebra: Example Sheet 3 of 4 1. Find the eigenvalues and give bases for the eigenspaces of the following complex matrices: 0 1 1 0 1 0 1 1 −1 1 0 1 1 −1 1 @ 0 3 −2 A ; @ 0 3 −2 A ; @ −1 3 −1 A : 0 1 0 0 1 0 −1 1 1 The second and third matrices commute; find a basis with respect to which they are both diagonal. 2. By considering the rank or minimal polynomial of a suitable matrix, find the eigenvalues of the n × n matrix A with each diagonal entry equal to λ and all other entries 1. Hence write down the determinant of A. 3. Let A be an n × n matrix all the entries of which are real. Show that the minimum polynomial of A, over the complex numbers, has real coefficients. 4. (i) Let V be a vector space, let π1; π2; : : : ; πk be endomorphisms of V such that idV = π1 + ··· + πk and πiπj = 0 for any i 6= j. Show that V = U1 ⊕ · · · ⊕ Uk, where Uj = Im(πj). (ii) Let α be an endomorphism of V satisfying the equation α3 = α. By finding suitable endomorphisms of V depending on α, use (i) to prove that V = V0 ⊕ V1 ⊕ V−1, where Vλ is the λ-eigenspace of α. (iii) Apply (i) to prove the Generalised Eigenspace Decomposition Theorem stated in lectures. [Hint: It may help to re-read the proof of the Diagonalisability Theorem given in lectures. You may use the fact that if p1(t); : : : ; pk(t) 2 C[t] are non-zero polynomials with no common factor, Euclid's algorithm implies that there exist polynomials q1(t); : : : ; qk(t) 2 C[t] such that p1(t)q1(t) + ::: + pk(t)qk(t) = 1.] 5.