Circulant Matrices and Affine Equivalence of Monomial Rotation Symmetric Boolean Functions
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Modeling, Inference and Clustering for Equivalence Classes of 3-D Orientations Chuanlong Du Iowa State University
Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2014 Modeling, inference and clustering for equivalence classes of 3-D orientations Chuanlong Du Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Statistics and Probability Commons Recommended Citation Du, Chuanlong, "Modeling, inference and clustering for equivalence classes of 3-D orientations" (2014). Graduate Theses and Dissertations. 13738. https://lib.dr.iastate.edu/etd/13738 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Modeling, inference and clustering for equivalence classes of 3-D orientations by Chuanlong Du A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Statistics Program of Study Committee: Stephen Vardeman, Co-major Professor Daniel Nordman, Co-major Professor Dan Nettleton Huaiqing Wu Guang Song Iowa State University Ames, Iowa 2014 Copyright c Chuanlong Du, 2014. All rights reserved. ii DEDICATION I would like to dedicate this dissertation to my mother Jinyan Xu. She always encourages me to follow my heart and to seek my dreams. Her words have always inspired me and encouraged me to face difficulties and challenges I came across in my life. I would also like to dedicate this dissertation to my wife Lisha Li without whose support and help I would not have been able to complete this work. -
Sometimes Only Square Matrices Can Be Diagonalized 19
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 52, October 1975 SOMETIMESONLY SQUARE MATRICESCAN BE DIAGONALIZED LAWRENCE S. LEVY ABSTRACT. It is proved that every SQUARE matrix over a serial ring is equivalent to some diagonal matrix, even though there are rectangular matri- ces over these rings which cannot be diagonalized. Two matrices A and B over a ring R are equivalent if B - PAQ for in- vertible matrices P and Q over R- Following R. B. Warfield, we will call a module serial if its submodules are totally ordered by inclusion; and we will call a ring R serial it R is a direct sum of serial left modules, and also a direct sum of serial right mod- ules. When R is artinian, these become the generalized uniserial rings of Nakayama LE-GJ- Note that, in serial rings, finitely generated ideals do not have to be principal; for example, let R be any full ring of lower triangular matrices over a field. Moreover, it is well known (and easily proved) that if aR + bR is a nonprincipal right ideal in any ring, the 1x2 matrix [a b] is not equivalent to a diagonal matrix. Thus serial rings do not have the property that every rectangular matrix can be diagonalized. 1. Background. Serial rings are clearly semiperfect (P/radR is semisim- ple artinian and idempotents can be lifted modulo radP). (1.1) Every indecomposable projective module over a semiperfect ring R is S eR for some primitive idempotent e of R. (See [M].) A double-headed arrow -» will denote an "onto" homomorphism. -
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. -
The Intersection of Some Classical Equivalence Classes of Matrices Mark Alan Mills Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1999 The intersection of some classical equivalence classes of matrices Mark Alan Mills Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Mathematics Commons Recommended Citation Mills, Mark Alan, "The intersection of some classical equivalence classes of matrices " (1999). Retrospective Theses and Dissertations. 12153. https://lib.dr.iastate.edu/rtd/12153 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly fi'om the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing firom left to right in equal sections with small overlaps. -
A Review of Matrix Scaling and Sinkhorn's Normal Form for Matrices
A review of matrix scaling and Sinkhorn’s normal form for matrices and positive maps Martin Idel∗ Zentrum Mathematik, M5, Technische Universität München, 85748 Garching Abstract Given a nonnegative matrix A, can you find diagonal matrices D1, D2 such that D1 AD2 is doubly stochastic? The answer to this question is known as Sinkhorn’s theorem. It has been proved with a wide variety of methods, each presenting a variety of possible generalisations. Recently, generalisations such as to positive maps between matrix algebras have become more and more interesting for applications. This text gives a review of over 70 years of matrix scaling. The focus lies on the mathematical landscape surrounding the problem and its solution as well as the generalisation to positive maps and contains hardly any nontrivial unpublished results. Contents 1. Introduction3 2. Notation and Preliminaries4 3. Different approaches to equivalence scaling5 3.1. Historical remarks . .6 3.2. The logarithmic barrier function . .8 3.3. Nonlinear Perron-Frobenius theory . 12 3.4. Entropy optimisation . 14 3.5. Convex programming and dual problems . 17 3.6. Topological (non-constructive) approaches . 20 arXiv:1609.06349v1 [math.RA] 20 Sep 2016 3.7. Other ideas . 21 3.7.1. Geometric proofs . 22 3.7.2. Other direct convergence proofs . 23 ∗[email protected] 1 4. Equivalence scaling 24 5. Other scalings 27 5.1. Matrix balancing . 27 5.2. DAD scaling . 29 5.3. Matrix Apportionment . 33 5.4. More general matrix scalings . 33 6. Generalised approaches 35 6.1. Direct multidimensional scaling . 35 6.2. Log-linear models and matrices as vectors . -
Math 623: Matrix Analysis Final Exam Preparation
Mike O'Sullivan Department of Mathematics San Diego State University Spring 2013 Math 623: Matrix Analysis Final Exam Preparation The final exam has two parts, which I intend to each take one hour. Part one is on the material covered since the last exam: Determinants; normal, Hermitian and positive definite matrices; positive matrices and Perron's theorem. The problems will all be very similar to the ones in my notes, or in the accompanying list. For part two, you will write an essay on what I see as the fundamental theme of the course, the four equivalence relations on matrices: matrix equivalence, similarity, unitary equivalence and unitary similarity (See p. 41 in Horn 2nd Ed. He calls matrix equivalence simply \equivalence."). Imagine you are writing for a fellow master's student. Your goal is to explain to them the key ideas. Matrix equivalence: (1) Define it. (2) Explain the relationship with abstract linear transformations and change of basis. (3) We found a simple set of representatives for the equivalence classes: identify them and sketch the proof. Similarity: (1) Define it. (2) Explain the relationship with abstract linear transformations and change of basis. (3) We found a set of representatives for the equivalence classes using Jordan matrices. State the Jordan theorem. (4) The proof of the Jordan theorem can be broken into two parts: (1) writing the ambient space as a direct sum of generalized eigenspaces, (2) classifying nilpotent matrices. Sketch the proof of each. Explain the role of invariant spaces. Unitary equivalence (1) Define it. (2) Explain the relationship with abstract inner product spaces and change of basis. -
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. -
A Local Approach to Matrix Equivalence*
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector A Local Approach to Matrix Equivalence* Larry J. Gerstein Department of Mathematics University of California Santa Barbara, California 93106 Submitted by Hans Schneider ABSTRACT Matrix equivalence over principal ideal domains is considered, using the tech- nique of localization from commutative algebra. This device yields short new proofs for a variety of results. (Some of these results were known earlier via the theory of determinantal divisors.) A new algorithm is presented for calculation of the Smith normal form of a matrix, and examples are included. Finally, the natural analogue of the Witt-Grothendieck ring for quadratic forms is considered in the context of matrix equivalence. INTRODUCTION It is well known that every matrix A over a principal ideal domain is equivalent to a diagonal matrix S(A) whose main diagonal entries s,(A),s,(A), . divide each other in succession. [The s,(A) are the invariant factors of A; they are unique up to unit multiples.] How should one effect this diagonalization? If the ring is Euclidean, a sequence of elementary row and column operations will do the job; but in the general case one usually relies on the theory of determinantal divisors: Set d,,(A) = 1, and for 1 < k < r = rankA define d,(A) to be the greatest common divisor of all the k x k subdeterminants of A; then +(A) = dk(A)/dk_-l(A) for 1 < k < r, and sk(A) = 0 if k > r. The calculation of invariant factors via determinantal divisors in this way is clearly a very tedious process. -
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. -
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. -
Factoring Matrices Into the Product of Circulant and Diagonal Matrices
FACTORING MATRICES INTO THE PRODUCT OF CIRCULANT AND DIAGONAL MATRICES MARKO HUHTANEN∗ AND ALLAN PERAM¨ AKI¨ y n×n Abstract. A generic matrix A 2 C is shown to be the product of circulant and diagonal matrices with the number of factors being 2n−1 at most. The demonstration is constructive, relying on first factoring matrix subspaces equivalent to polynomials in a permutation matrix over diagonal matrices into linear factors. For the linear factors, the sum of two scaled permutations is factored into the product of a circulant matrix and two diagonal matrices. Extending the monomial group, both low degree and sparse polynomials in a permutation matrix over diagonal matrices, together with their permutation equivalences, constitute a fundamental sparse matrix structure. Matrix analysis gets largely done polynomially, in terms of permutations only. Key words. circulant matrix, diagonal matrix, DFT, Fourier optics, sparsity structure, matrix factoring, polynomial factoring, multiplicative Fourier compression AMS subject classifications. 15A23, 65T50, 65F50, 12D05 1. Introduction. There exists an elegant result, motivated by applications in optical image processing, stating that any matrix A 2 Cn×n is the product of circu- lant and diagonal matrices [15, 18].1 In this paper it is shown that, generically, 2n − 1 factors suffice. (For various aspects of matrix factoring, see [13].) The demonstration is constructive, relying on first factoring matrix subspaces equivalent to polynomials in a permutation matrix over diagonal matrices into linear factors. This is achieved by solving structured systems of polynomial equations. Located on the borderline between commutative and noncommutative algebra, such subspaces are shown to constitute a fundamental sparse matrix structure of polynomial type extending, e.g., band matrices.