Matrix Semigroups Over Semirings
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Perron–Frobenius Theory of Nonnegative Matrices
Buy from AMAZON.com http://www.amazon.com/exec/obidos/ASIN/0898714540 CHAPTER 8 Perron–Frobenius Theory of Nonnegative Matrices 8.1 INTRODUCTION m×n A ∈ is said to be a nonnegative matrix whenever each aij ≥ 0, and this is denoted by writing A ≥ 0. In general, A ≥ B means that each aij ≥ bij. Similarly, A is a positive matrix when each aij > 0, and this is denoted by writing A > 0. More generally, A > B means that each aij >bij. Applications abound with nonnegative and positive matrices. In fact, many of the applications considered in this text involve nonnegative matrices. For example, the connectivity matrix C in Example 3.5.2 (p. 100) is nonnegative. The discrete Laplacian L from Example 7.6.2 (p. 563) leads to a nonnegative matrix because (4I − L) ≥ 0. The matrix eAt that defines the solution of the system of differential equations in the mixing problem of Example 7.9.7 (p. 610) is nonnegative for all t ≥ 0. And the system of difference equations p(k)=Ap(k − 1) resulting from the shell game of Example 7.10.8 (p. 635) has Please report violations to [email protected] a nonnegativeCOPYRIGHTED coefficient matrix A. Since nonnegative matrices are pervasive, it’s natural to investigate their properties, and that’s the purpose of this chapter. A primary issue concerns It is illegal to print, duplicate, or distribute this material the extent to which the properties A > 0 or A ≥ 0 translate to spectral properties—e.g., to what extent does A have positive (or nonnegative) eigen- values and eigenvectors? The topic is called the “Perron–Frobenius theory” because it evolved from the contributions of the German mathematicians Oskar (or Oscar) Perron 89 and 89 Oskar Perron (1880–1975) originally set out to fulfill his father’s wishes to be in the family busi- Buy online from SIAM Copyright c 2000 SIAM http://www.ec-securehost.com/SIAM/ot71.html Buy from AMAZON.com 662 Chapter 8 Perron–Frobenius Theory of Nonnegative Matrices http://www.amazon.com/exec/obidos/ASIN/0898714540 Ferdinand Georg Frobenius. -
Relations in Categories
Relations in Categories Stefan Milius A thesis submitted to the Faculty of Graduate Studies in partial fulfilment of the requirements for the degree of Master of Arts Graduate Program in Mathematics and Statistics York University Toronto, Ontario June 15, 2000 Abstract This thesis investigates relations over a category C relative to an (E; M)-factori- zation system of C. In order to establish the 2-category Rel(C) of relations over C in the first part we discuss sufficient conditions for the associativity of horizontal composition of relations, and we investigate special classes of morphisms in Rel(C). Attention is particularly devoted to the notion of mapping as defined by Lawvere. We give a significantly simplified proof for the main result of Pavlovi´c,namely that C Map(Rel(C)) if and only if E RegEpi(C). This part also contains a proof' that the category Map(Rel(C))⊆ is finitely complete, and we present the results obtained by Kelly, some of them generalized, i. e., without the restrictive assumption that M Mono(C). The next part deals with factorization⊆ systems in Rel(C). The fact that each set-relation has a canonical image factorization is generalized and shown to yield an (E¯; M¯ )-factorization system in Rel(C) in case M Mono(C). The setting without this condition is studied, as well. We propose a⊆ weaker notion of factorization system for a 2-category, where the commutativity in the universal property of an (E; M)-factorization system is replaced by coherent 2-cells. In the last part certain limits and colimits in Rel(C) are investigated. -
4.6 Matrices Dimensions: Number of Rows and Columns a Is a 2 X 3 Matrix
Matrices are rectangular arrangements of data that are used to represent information in tabular form. 1 0 4 A = 3 - 6 8 a23 4.6 Matrices Dimensions: number of rows and columns A is a 2 x 3 matrix Elements of a matrix A are denoted by aij. a23 = 8 1 2 Data about many kinds of problems can often be represented by Matrix of coefficients matrix. Solutions to many problems can be obtained by solving e.g Average temperatures in 3 different cities for each month: systems of linear equations. For example, the constraints of a problem are represented by the system of linear equations 23 26 38 47 58 71 78 77 69 55 39 33 x + y = 70 A = 14 21 33 38 44 57 61 59 49 38 25 21 3 cities 24x + 14y = 1180 35 46 54 67 78 86 91 94 89 75 62 51 12 months Jan - Dec 1 1 rd The matrix A = Average temp. in the 3 24 14 city in July, a37, is 91. is the matrix of coefficient for this system of linear equations. 3 4 In a matrix, the arrangement of the entries is significant. Square Matrix is a matrix in which the number of Therefore, for two matrices to be equal they must have rows equals the number of columns. the same dimensions and the same entries in each location. •Main Diagonal: in a n x n square matrix, the elements a , a , a , …, a form the main Example: Let 11 22 33 nn diagonal of the matrix. -
Littlewood–Richardson Coefficients and Birational Combinatorics
Littlewood{Richardson coefficients and birational combinatorics Darij Grinberg 28 August 2020 [corrected version] Algebraic and Combinatorial Perspectives in the Mathematical Sciences slides: http: //www.cip.ifi.lmu.de/~grinberg/algebra/acpms2020.pdf paper: arXiv:2008.06128 aka http: //www.cip.ifi.lmu.de/~grinberg/algebra/lrhspr.pdf 1 / 43 The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. 2 / 43 Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). 2 / 43 Manifest I shall review the Littlewood{Richardson coefficients and some of their classical properties. I will then state a \hidden symmetry" conjectured by Pelletier and Ressayre (arXiv:2005.09877) and outline how I proved it. The proof is a nice example of birational combinatorics: the use of birational transformations in elementary combinatorics (specifically, here, in finding and proving a bijection). 2 / 43 Chapter 1 Chapter 1 Littlewood{Richardson coefficients References (among many): Richard Stanley, Enumerative Combinatorics, vol. 2, Chapter 7. Darij Grinberg, Victor Reiner, Hopf Algebras in Combinatorics, arXiv:1409.8356. -
Recent Developments in Boolean Matrix Factorization
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Survey Track Recent Developments in Boolean Matrix Factorization Pauli Miettinen1∗ and Stefan Neumann2y 1University of Eastern Finland, Kuopio, Finland 2University of Vienna, Vienna, Austria pauli.miettinen@uef.fi, [email protected] Abstract analysts, and van den Broeck and Darwiche [2013] to lifted inference community, to name but a few examples. Even more The goal of Boolean Matrix Factorization (BMF) is recently, Ravanbakhsh et al. [2016] studied the problem in to approximate a given binary matrix as the product the framework of machine learning, increasing the interest of of two low-rank binary factor matrices, where the that community, and Chandran et al. [2016]; Ban et al. [2019]; product of the factor matrices is computed under Fomin et al. [2019] (re-)launched the interest in the problem the Boolean algebra. While the problem is computa- in the theory community. tionally hard, it is also attractive because the binary The various fields in which BMF is used are connected by nature of the factor matrices makes them highly in- the desire to “keep the data binary”. This can be because terpretable. In the last decade, BMF has received a the factorization is used as a pre-processing step, and the considerable amount of attention in the data mining subsequent methods require binary input, or because binary and formal concept analysis communities and, more matrices are more interpretable in the application domain. In recently, the machine learning and the theory com- the latter case, Boolean algebra is often preferred over the field munities also started studying BMF. -
Geometry, Combinatorial Designs and Cryptology Fourth Pythagorean Conference
Geometry, Combinatorial Designs and Cryptology Fourth Pythagorean Conference Sunday 30 May to Friday 4 June 2010 Index of Talks and Abstracts Main talks 1. Simeon Ball, On subsets of a finite vector space in which every subset of basis size is a basis 2. Simon Blackburn, Honeycomb arrays 3. G`abor Korchm`aros, Curves over finite fields, an approach from finite geometry 4. Cheryl Praeger, Basic pregeometries 5. Bernhard Schmidt, Finiteness of circulant weighing matrices of fixed weight 6. Douglas Stinson, Multicollision attacks on iterated hash functions Short talks 1. Mari´en Abreu, Adjacency matrices of polarity graphs and of other C4–free graphs of large size 2. Marco Buratti, Combinatorial designs via factorization of a group into subsets 3. Mike Burmester, Lightweight cryptographic mechanisms based on pseudorandom number generators 4. Philippe Cara, Loops, neardomains, nearfields and sets of permutations 5. Ilaria Cardinali, On the structure of Weyl modules for the symplectic group 6. Bill Cherowitzo, Parallelisms of quadrics 7. Jan De Beule, Large maximal partial ovoids of Q−(5, q) 8. Bart De Bruyn, On extensions of hyperplanes of dual polar spaces 1 9. Frank De Clerck, Intriguing sets of partial quadrangles 10. Alice Devillers, Symmetry properties of subdivision graphs 11. Dalibor Froncek, Decompositions of complete bipartite graphs into generalized prisms 12. Stelios Georgiou, Self-dual codes from circulant matrices 13. Robert Gilman, Cryptology of infinite groups 14. Otokar Groˇsek, The number of associative triples in a quasigroup 15. Christoph Hering, Latin squares, homologies and Euler’s conjecture 16. Leanne Holder, Bilinear star flocks of arbitrary cones 17. Robert Jajcay, On the geometry of cages 18. -
What Are Kinship Terminologies, and Why Do We Care?: a Computational Approach To
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Kent Academic Repository What are Kinship Terminologies, and Why do we Care?: A Computational Approach to Analysing Symbolic Domains Dwight Read, UCLA Murray Leaf, University of Texas, Dallas Michael Fischer, University of Kent, Canterbury, Corresponding Author, [email protected] Abstract Kinship is a fundamental feature and basis of human societies. We describe a set of computat ional tools and services, the Kinship Algebra Modeler, and the logic that underlies these. Thes e were developed to improve how we understand both the fundamental facts of kinship, and h ow people use kinship as a resource in their lives. Mathematical formalism applied to cultural concepts is more than an exercise in model building, as it provides a way to represent and exp lore logical consistency and implications. The logic underlying kinship is explored here thro ugh the kin term computations made by users of a terminology when computing the kinship r elation one person has to another by referring to a third person for whom each has a kin term relationship. Kinship Algebra Modeler provides a set of tools, services and an architecture to explore kinship terminologies and their properties in an accessible manner. Keywords Kinship, Algebra, Semantic Domains, Kinship Terminology, Theory Building 1 1. Introduction The Kinship Algebra Modeller (KAM) is a suite of open source software tools and services u nder development to support the elicitation and analysis of kinship terminologies, building al gebraic models of the relations structuring terminologies, and instantiating these models in lar ger contexts to better understand how people pragmatically interpret and employ the logic of kinship relations as a resource in their individual and collective lives. -
Parametrizations of Canonical Bases and Totally Positive Matrices Arkady Berenstein
Advances in Mathematics AI1567 advances in mathematics 122, 49149 (1996) article no. 0057 Parametrizations of Canonical Bases and Totally Positive Matrices Arkady Berenstein Department of Mathematics, Northeastern University, Boston, Massachusetts 02115 Sergey Fomin* Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; and Theory of Algorithms Laboratory, St. Petersburg Institute of Informatics, Russian Academy of Sciences, St. Petersburg, Russia and Andrei Zelevinsky- Department of Mathematics, Northeastern University, Boston, Massachusetts 02115 Received October 24, 1995; accepted November 27, 1995 Contents. 1. Introduction and main results. 2. Lusztig variety and Chamber Ansatz. 2.1. Semifields and subtraction-free rational expressions. 2.2. Lusztig variety. 2.3. Pseudo-line arrangements. 2.4. Minors and the nilTemperleyLieb algebra. 2.5. Chamber Ansatz. 2.6. Solutions of the 3-term relations. 2.7. An alternative description of the Lusztig variety. 2.8. Trans- 0 n n ition from n . 2.9. Polynomials T J and Za . 2.10. Formulas for T J . 2.11. Sym- metries of the Lusztig variety. 3. Total positivity criteria. 3.1. Factorization of unitriangular matrices. 3.2. General- izations of Fekete's criterion. 3.3. Polynomials TJ (x). 3.4. The action of the four- group on N >0. 3.5. The multi-filtration in the coordinate ring of N. 3.6. The S3 _ZÂ2Z symmetry. 3.7. Dual canonical basis and positivity theorem. 4. Piecewise-linear minimization formulas. 4.1. Polynomials over the tropical semi- field. 4.2. Multisegment duality. 4.3. Nested normal orderings. 4.4. Quivers and boundary pseudo-line arrangements. 5. The case of an arbitrary permutation. -
A Complete Bibliography of Publications in Linear Algebra and Its Applications: 2010–2019
A Complete Bibliography of Publications in Linear Algebra and its Applications: 2010{2019 Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 12 March 2021 Version 1.74 Title word cross-reference KY14, Rim12, Rud12, YHH12, vdH14]. 24 [KAAK11]. 2n − 3[BCS10,ˇ Hil13]. 2 × 2 [CGRVC13, CGSCZ10, CM14, DW11, DMS10, JK11, KJK13, MSvW12, Yan14]. (−1; 1) [AAFG12].´ (0; 1) 2 × 2 × 2 [Ber13b]. 3 [BBS12b, NP10, Ghe14a]. (2; 2; 0) [BZWL13, Bre14, CILL12, CKAC14, Fri12, [CI13, PH12]. (A; B) [PP13b]. (α, β) GOvdD14, GX12a, Kal13b, KK14, YHH12]. [HW11, HZM10]. (C; λ; µ)[dMR12].(`; m) p 3n2 − 2 2n3=2 − 3n [MR13]. [DFG10]. (H; m)[BOZ10].(κ, τ) p 3n2 − 2 2n3=23n [MR14a]. 3 × 3 [CSZ10, CR10c]. (λ, 2) [BBS12b]. (m; s; 0) [Dru14, GLZ14, Sev14]. 3 × 3 × 2 [Ber13b]. [GH13b]. (n − 3) [CGO10]. (n − 3; 2; 1) 3 × 3 × 3 [BH13b]. 4 [Ban13a, BDK11, [CCGR13]. (!) [CL12a]. (P; R)[KNS14]. BZ12b, CK13a, FP14, NSW13, Nor14]. 4 × 4 (R; S )[Tre12].−1 [LZG14]. 0 [AKZ13, σ [CJR11]. 5 Ano12-30, CGGS13, DLMZ14, Wu10a]. 1 [BH13b, CHY12, KRH14, Kol13, MW14a]. [Ano12-30, AHL+14, CGGS13, GM14, 5 × 5 [BAD09, DA10, Hil12a, Spe11]. 5 × n Kal13b, LM12, Wu10a]. 1=n [CNPP12]. [CJR11]. 6 1 <t<2 [Seo14]. 2 [AIS14, AM14, AKA13, [DK13c, DK11, DK12a, DK13b, Kar11a]. -
Non Associative Linear Algebras
NON ASSOCIATIVE LINEAR ALGEBRAS W. B. Vasantha Kandasamy Florentin Smarandache ZIP PUBLISHING Ohio 2012 This book can be ordered from: Zip Publishing 1313 Chesapeake Ave. Columbus, Ohio 43212, USA Toll Free: (614) 485-0721 E-mail: [email protected] Website: www.zippublishing.com Copyright 2012 by Zip Publishing and the Authors Peer reviewers: Sukanto Bhattacharya, Deakin Graduate School of Business, Deakin University, Australia Kuldeep Kumar, School of Business, Bond University, Australia Professor Paul P. Wang, Ph D, Department of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, Durham, NC 27708, USA Many books can be downloaded from the following Digital Library of Science: http://www.gallup.unm.edu/~smarandache/eBooks-otherformats.htm ISBN-13: 978-1-59973-176-6 EAN: 9781599731766 Printed in the United States of America 2 CONTENTS Preface 5 Chapter One BASIC CONCEPTS 7 Chapter Two NON ASSOCIATIVE SEMILINEAR ALGEBRAS 13 Chapter Three NON ASSOCIATIVE LINEAR ALGEBRAS 83 Chapter Four GROUPOID VECTOR SPACES 111 3 Chapter Five APPLICATION OF NON ASSOCIATIVE VECTOR SPACES / LINEAR ALGEBRAS 161 Chapter Six SUGGESTED PROBLEMS 163 FURTHER READING 225 INDEX 229 ABOUT THE AUTHORS 231 4 PREFACE In this book authors for the first time introduce the notion of non associative vector spaces and non associative linear algebras over a field. We construct non associative space using loops and groupoids over fields. In general in all situations, which we come across to find solutions may not be associative; in such cases we can without any difficulty adopt these non associative vector spaces/linear algebras. Thus this research is a significant one. -
On Semifields of Type
I I G ◭◭ ◮◮ ◭ ◮ On semifields of type (q2n,qn,q2,q2,q), n odd page 1 / 19 ∗ go back Giuseppe Marino Olga Polverino Rocco Tromebtti full screen Abstract 2n n 2 2 close A semifield of type (q , q , q , q , q) (with n > 1) is a finite semi- field of order q2n (q a prime power) with left nucleus of order qn, right 2 quit and middle nuclei both of order q and center of order q. Semifields of type (q6, q3, q2, q2, q) have been completely classified by the authors and N. L. Johnson in [10]. In this paper we determine, up to isotopy, the form n n of any semifield of type (q2 , q , q2, q2, q) when n is an odd integer, proving n−1 that there exist 2 non isotopic potential families of semifields of this type. Also, we provide, with the aid of the computer, new examples of semifields of type (q14, q7, q2, q2, q), when q = 2. Keywords: semifield, isotopy, linear set MSC 2000: 51A40, 51E20, 12K10 1. Introduction A finite semifield S is a finite algebraic structure satisfying all the axioms for a skew field except (possibly) associativity. The subsets Nl = {a ∈ S | (ab)c = a(bc), ∀b, c ∈ S} , Nm = {b ∈ S | (ab)c = a(bc), ∀a, c ∈ S} , Nr = {c ∈ S | (ab)c = a(bc), ∀a, b ∈ S} and K = {a ∈ Nl ∩ Nm ∩ Nr | ab = ba, ∀b ∈ S} are fields and are known, respectively, as the left nucleus, the middle nucleus, the right nucleus and the center of the semifield. -
Foundations of Relations and Kleene Algebra
Introduction Foundations of Relations and Kleene Algebra Aim: cover the basics about relations and Kleene algebras within the framework of universal algebra Peter Jipsen This is a tutorial Slides give precise definitions, lots of statements Decide which statements are true (can be improved) Chapman University which are false (and perhaps how they can be fixed) [Hint: a list of pages with false statements is at the end] September 4, 2006 Peter Jipsen (Chapman University) Relation algebras and Kleene algebra September 4, 2006 1 / 84 Peter Jipsen (Chapman University) Relation algebras and Kleene algebra September 4, 2006 2 / 84 Prerequisites Algebraic properties of set operation Knowledge of sets, union, intersection, complementation Some basic first-order logic Let U be a set, and P(U)= {X : X ⊆ U} the powerset of U Basic discrete math (e.g. function notation) P(U) is an algebra with operations union ∪, intersection ∩, These notes take an algebraic perspective complementation X − = U \ X Satisfies many identities: e.g. X ∪ Y = Y ∪ X for all X , Y ∈ P(U) Conventions: How can we describe the set of all identities that hold? Minimize distinction between concrete and abstract notation x, y, z, x1,... variables (implicitly universally quantified) Can we decide if a particular identity holds in all powerset algebras? X , Y , Z, X1,... set variables (implicitly universally quantified) These are questions about the equational theory of these algebras f , g, h, f1,... function variables We will consider similar questions about several other types of algebras, a, b, c, a1,... constants in particular relation algebras and Kleene algebras i, j, k, i1,..