Products of Elementary and Idempotent Matrices Over Integral Domains ✩
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7. Euclidean Domains Let R Be an Integral Domain. We Want to Find Natural Conditions on R Such That R Is a PID. Looking at the C
7. Euclidean Domains Let R be an integral domain. We want to find natural conditions on R such that R is a PID. Looking at the case of the integers, it is clear that the key property is the division algorithm. Definition 7.1. Let R be an integral domain. We say that R is Eu- clidean, if there is a function d: R − f0g −! N [ f0g; which satisfies, for every pair of non-zero elements a and b of R, (1) d(a) ≤ d(ab): (2) There are elements q and r of R such that b = aq + r; where either r = 0 or d(r) < d(a). Example 7.2. The ring Z is a Euclidean domain. The function d is the absolute value. Definition 7.3. Let R be a ring and let f 2 R[x] be a polynomial with coefficients in R. The degree of f is the largest n such that the coefficient of xn is non-zero. Lemma 7.4. Let R be an integral domain and let f and g be two elements of R[x]. Then the degree of fg is the sum of the degrees of f and g. In particular R[x] is an integral domain. Proof. Suppose that f has degree m and g has degree n. If a is the leading coefficient of f and b is the leading coefficient of g then f = axm + ::: and f = bxn + :::; where ::: indicate lower degree terms then fg = (ab)xm+n + :::: As R is an integral domain, ab 6= 0, so that the degree of fg is m + n. -
Chapter Four Determinants
Chapter Four Determinants In the first chapter of this book we considered linear systems and we picked out the special case of systems with the same number of equations as unknowns, those of the form T~x = ~b where T is a square matrix. We noted a distinction between two classes of T ’s. While such systems may have a unique solution or no solutions or infinitely many solutions, if a particular T is associated with a unique solution in any system, such as the homogeneous system ~b = ~0, then T is associated with a unique solution for every ~b. We call such a matrix of coefficients ‘nonsingular’. The other kind of T , where every linear system for which it is the matrix of coefficients has either no solution or infinitely many solutions, we call ‘singular’. Through the second and third chapters the value of this distinction has been a theme. For instance, we now know that nonsingularity of an n£n matrix T is equivalent to each of these: ² a system T~x = ~b has a solution, and that solution is unique; ² Gauss-Jordan reduction of T yields an identity matrix; ² the rows of T form a linearly independent set; ² the columns of T form a basis for Rn; ² any map that T represents is an isomorphism; ² an inverse matrix T ¡1 exists. So when we look at a particular square matrix, the question of whether it is nonsingular is one of the first things that we ask. This chapter develops a formula to determine this. (Since we will restrict the discussion to square matrices, in this chapter we will usually simply say ‘matrix’ in place of ‘square matrix’.) More precisely, we will develop infinitely many formulas, one for 1£1 ma- trices, one for 2£2 matrices, etc. -
Abstract Algebra: Monoids, Groups, Rings
Notes on Abstract Algebra John Perry University of Southern Mississippi [email protected] http://www.math.usm.edu/perry/ Copyright 2009 John Perry www.math.usm.edu/perry/ Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States You are free: to Share—to copy, distribute and transmit the work • to Remix—to adapt the work Under• the following conditions: Attribution—You must attribute the work in the manner specified by the author or licen- • sor (but not in any way that suggests that they endorse you or your use of the work). Noncommercial—You may not use this work for commercial purposes. • Share Alike—If you alter, transform, or build upon this work, you may distribute the • resulting work only under the same or similar license to this one. With the understanding that: Waiver—Any of the above conditions can be waived if you get permission from the copy- • right holder. Other Rights—In no way are any of the following rights affected by the license: • Your fair dealing or fair use rights; ◦ Apart from the remix rights granted under this license, the author’s moral rights; ◦ Rights other persons may have either in the work itself or in how the work is used, ◦ such as publicity or privacy rights. Notice—For any reuse or distribution, you must make clear to others the license terms of • this work. The best way to do this is with a link to this web page: http://creativecommons.org/licenses/by-nc-sa/3.0/us/legalcode Table of Contents Reference sheet for notation...........................................................iv A few acknowledgements..............................................................vi Preface ...............................................................................vii Overview ...........................................................................vii Three interesting problems ............................................................1 Part . -
2014 CBK Linear Algebra Honors.Pdf
PETERS TOWNSHIP SCHOOL DISTRICT CORE BODY OF KNOWLEDGE LINEAR ALGEBRA HONORS GRADE 12 For each of the sections that follow, students may be required to analyze, recall, explain, interpret, apply, or evaluate the particular concept being taught. Course Description This college level mathematics course will cover linear algebra and matrix theory emphasizing topics useful in other disciplines such as physics and engineering. Key topics include solving systems of equations, evaluating vector spaces, performing linear transformations and matrix representations. Linear Algebra Honors is designed for the extremely capable student who has completed one year of calculus. Systems of Linear Equations Categorize a linear equation in n variables Formulate a parametric representation of solution set Assess a system of linear equations to determine if it is consistent or inconsistent Apply concepts to use back-substitution and Guassian elimination to solve a system of linear equations Investigate the size of a matrix and write an augmented or coefficient matrix from a system of linear equations Apply concepts to use matrices and Guass-Jordan elimination to solve a system of linear equations Solve a homogenous system of linear equations Design, setup and solve a system of equations to fit a polynomial function to a set of data points Design, set up and solve a system of equations to represent a network Matrices Categorize matrices as equal Construct a sum matrix Construct a product matrix Assess two matrices as compatible Apply matrix multiplication -
Section 2.4–2.5 Partitioned Matrices and LU Factorization
Section 2.4{2.5 Partitioned Matrices and LU Factorization Gexin Yu [email protected] College of William and Mary Gexin Yu [email protected] Section 2.4{2.5 Partitioned Matrices and LU Factorization One approach to simplify the computation is to partition a matrix into blocks. 2 3 0 −1 5 9 −2 3 Ex: A = 4 −5 2 4 0 −3 1 5. −8 −6 3 1 7 −4 This partition can also be written as the following 2 × 3 block matrix: A A A A = 11 12 13 A21 A22 A23 3 0 −1 In the block form, we have blocks A = and so on. 11 −5 2 4 partition matrices into blocks In real world problems, systems can have huge numbers of equations and un-knowns. Standard computation techniques are inefficient in such cases, so we need to develop techniques which exploit the internal structure of the matrices. In most cases, the matrices of interest have lots of zeros. Gexin Yu [email protected] Section 2.4{2.5 Partitioned Matrices and LU Factorization 2 3 0 −1 5 9 −2 3 Ex: A = 4 −5 2 4 0 −3 1 5. −8 −6 3 1 7 −4 This partition can also be written as the following 2 × 3 block matrix: A A A A = 11 12 13 A21 A22 A23 3 0 −1 In the block form, we have blocks A = and so on. 11 −5 2 4 partition matrices into blocks In real world problems, systems can have huge numbers of equations and un-knowns. -
Linear Algebra and Matrix Theory
Linear Algebra and Matrix Theory Chapter 1 - Linear Systems, Matrices and Determinants This is a very brief outline of some basic definitions and theorems of linear algebra. We will assume that you know elementary facts such as how to add two matrices, how to multiply a matrix by a number, how to multiply two matrices, what an identity matrix is, and what a solution of a linear system of equations is. Hardly any of the theorems will be proved. More complete treatments may be found in the following references. 1. References (1) S. Friedberg, A. Insel and L. Spence, Linear Algebra, Prentice-Hall. (2) M. Golubitsky and M. Dellnitz, Linear Algebra and Differential Equa- tions Using Matlab, Brooks-Cole. (3) K. Hoffman and R. Kunze, Linear Algebra, Prentice-Hall. (4) P. Lancaster and M. Tismenetsky, The Theory of Matrices, Aca- demic Press. 1 2 2. Linear Systems of Equations and Gaussian Elimination The solutions, if any, of a linear system of equations (2.1) a11x1 + a12x2 + ··· + a1nxn = b1 a21x1 + a22x2 + ··· + a2nxn = b2 . am1x1 + am2x2 + ··· + amnxn = bm may be found by Gaussian elimination. The permitted steps are as follows. (1) Both sides of any equation may be multiplied by the same nonzero constant. (2) Any two equations may be interchanged. (3) Any multiple of one equation may be added to another equation. Instead of working with the symbols for the variables (the xi), it is eas- ier to place the coefficients (the aij) and the forcing terms (the bi) in a rectangular array called the augmented matrix of the system. a11 a12 . -
Unit and Unitary Cayley Graphs for the Ring of Gaussian Integers Modulo N
Quasigroups and Related Systems 25 (2017), 189 − 200 Unit and unitary Cayley graphs for the ring of Gaussian integers modulo n Ali Bahrami and Reza Jahani-Nezhad Abstract. Let [i] be the ring of Gaussian integers modulo n and G( [i]) and G be the Zn Zn Zn[i] unit graph and the unitary Cayley graph of Zn[i], respectively. In this paper, we study G(Zn[i]) and G . Among many results, it is shown that for each positive integer n, the graphs G( [i]) Zn[i] Zn and G are Hamiltonian. We also nd a necessary and sucient condition for the unit and Zn[i] unitary Cayley graphs of Zn[i] to be Eulerian. 1. Introduction Finding the relationship between the algebraic structure of rings using properties of graphs associated to them has become an interesting topic in the last years. There are many papers on assigning a graph to a ring, see [1], [3], [4], [5], [7], [6], [8], [10], [11], [12], [17], [19], and [20]. Let R be a commutative ring with non-zero identity. We denote by U(R), J(R) and Z(R) the group of units of R, the Jacobson radical of R and the set of zero divisors of R, respectively. The unitary Cayley graph of R, denoted by GR, is the graph whose vertex set is R, and in which fa; bg is an edge if and only if a − b 2 U(R). The unit graph G(R) of R is the simple graph whose vertices are elements of R and two distinct vertices a and b are adjacent if and only if a + b in U(R) . -
NOTES on UNIQUE FACTORIZATION DOMAINS Alfonso Gracia-Saz, MAT 347
Unique-factorization domains MAT 347 NOTES ON UNIQUE FACTORIZATION DOMAINS Alfonso Gracia-Saz, MAT 347 Note: These notes summarize the approach I will take to Chapter 8. You are welcome to read Chapter 8 in the book instead, which simply uses a different order, and goes in slightly different depth at different points. If you read the book, notice that I will skip any references to universal side divisors and Dedekind-Hasse norms. If you find any typos or mistakes, please let me know. These notes complement, but do not replace lectures. Last updated on January 21, 2016. Note 1. Through this paper, we will assume that all our rings are integral domains. R will always denote an integral domains, even if we don't say it each time. Motivation: We know that every integer number is the product of prime numbers in a unique way. Sort of. We just believed our kindergarden teacher when she told us, and we omitted the fact that it needed to be proven. We want to prove that this is true, that something similar is true in the ring of polynomials over a field. More generally, in which domains is this true? In which domains does this fail? 1 Unique-factorization domains In this section we want to define what it means that \every" element can be written as product of \primes" in a \unique" way (as we normally think of the integers), and we want to see some examples where this fails. It will take us a few definitions. Definition 2. Let a; b 2 R. -
Euclidean Quadratic Forms and Adc-Forms: I
EUCLIDEAN QUADRATIC FORMS AND ADC-FORMS: I PETE L. CLARK We denote by N the non-negative integers (including 0). Throughout R will denote a commutative, unital integral domain and K its fraction • field. We write R for R n f0g and ΣR for the set of height one primes of R. If M and N are monoids (written multiplicatively, with identity element 1), a monoid homomorphism f : M ! N is nondegenerate if f(x) = 1 () x = 1. Introduction The goal of this work is to set up the foundations and begin the systematic arith- metic study of certain classes of quadratic forms over a fairly general class of integral domains. Our work here is concentrated around that of two definitions, that of Eu- clidean form and ADC form. These definitions have a classical flavor, and various special cases of them can be found (most often implicitly) in the literature. Our work was particularly motivated by the similarities between two classical theorems. × Theorem 1. (Aubry, Davenport-Cassels) LetP A = (aij) be a symmetric n n Z matrix with coefficients in , and let q(x) = 1≤i;j≤n aijxixj be a positive definite integral quadratic form. Suppose that for all x 2 Qn, there exists y 2 Zn such that q(x − y) < 1. Then if d 2 Z is such that there exists x 2 Qn with q(x) = d, there exists y 2 Zn such that q(y) = d. 2 2 2 Consider q(x) = x1 + x2 + x3. It satisfies the hypotheses of the theorem: approxi- mating a vector x 2 Q3 by a vector y 2 Z3 of nearest integer entries, we get 3 (x − y )2 + (x − y )2 + (x − y )2 ≤ < 1: 1 1 2 2 3 3 4 Thus Theorem 1 shows that every integer which is the sum of three rational squares is also the sum of three integral squares. -
Section IX.46. Euclidean Domains
IX.46. Euclidean Domains 1 Section IX.46. Euclidean Domains Note. Fraleigh comments at the beginning of this section: “Now a modern tech- nique of mathematics is to take some clearly related situations and try to bring them under one roof by abstracting the important ideas common to them.” In this section, we take the idea of the division algorithm for integral domain Z and generalize it to other integral domains. Note. Recall: 1. Division Algorithm for Z (Theorem 6.3). If m is a positive integer and n is any integer, then there exist unique integers q and r such that n = mq + r and 0 ≤ r < m. 2. Division Algorithm for [x] (Theorem 23.1). n n−1 Let F be a field and let f(x)= anx + an−1x + ··· + a1x + a0 and g(x)= n m−1 bnx + bm−1x + ···+ b1x + b0 be two elements of F [x], with an and bm both nonzero elements of F and m > 0. Then there are unique polynomials q(x) and r(x) in F [x] such that f(x) = g(x)q(x)+ r(x), where either r(x)=0or the degree of r(x) is less than the degree m of g(x). Note. We now introduce a function which maps an integral domain into the nonnegative integers and use the values of this function to replace the ideas of “remainder” in Z and “degree” in F [x]. IX.46. Euclidean Domains 2 Definition 46.1. A Euclidean norm on an integral domain D is a function v mapping the nonzero elements of D into the nonnegative integers such that the following conditions are satisfied: 1. -
Block Matrices in Linear Algebra
PRIMUS Problems, Resources, and Issues in Mathematics Undergraduate Studies ISSN: 1051-1970 (Print) 1935-4053 (Online) Journal homepage: https://www.tandfonline.com/loi/upri20 Block Matrices in Linear Algebra Stephan Ramon Garcia & Roger A. Horn To cite this article: Stephan Ramon Garcia & Roger A. Horn (2020) Block Matrices in Linear Algebra, PRIMUS, 30:3, 285-306, DOI: 10.1080/10511970.2019.1567214 To link to this article: https://doi.org/10.1080/10511970.2019.1567214 Accepted author version posted online: 05 Feb 2019. Published online: 13 May 2019. Submit your article to this journal Article views: 86 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=upri20 PRIMUS, 30(3): 285–306, 2020 Copyright # Taylor & Francis Group, LLC ISSN: 1051-1970 print / 1935-4053 online DOI: 10.1080/10511970.2019.1567214 Block Matrices in Linear Algebra Stephan Ramon Garcia and Roger A. Horn Abstract: Linear algebra is best done with block matrices. As evidence in sup- port of this thesis, we present numerous examples suitable for classroom presentation. Keywords: Matrix, matrix multiplication, block matrix, Kronecker product, rank, eigenvalues 1. INTRODUCTION This paper is addressed to instructors of a first course in linear algebra, who need not be specialists in the field. We aim to convince the reader that linear algebra is best done with block matrices. In particular, flexible thinking about the process of matrix multiplication can reveal concise proofs of important theorems and expose new results. Viewing linear algebra from a block-matrix perspective gives an instructor access to use- ful techniques, exercises, and examples. -
1.5 Elementary Matrices and a Method for Finding the Inverse
1.5 Elementary Matrices and a Method for Finding the Inverse De¯nition 1 A n £ n matrix is called an elementary matrix if it can be obtained from In by performing a single elementary row operation Reminder: Elementary row operations: 1. Multiply a row a by k 2 R 2. Exchange two rows 3. Add a multiple of one row to another Theorem 1 If the elementary matrix E results from performing a certain row operation on In and A is a m £ n matrix, then EA is the matrix that results when the same row operation is performed on A. Proof: Has to be done for each elementary row operation. Example 1 · ¸ · ¸ a b 0 1 A = ;E = c d 1 0 E was obtained from I2 by exchanging the two rows. · ¸ · ¸ · ¸ 0 1 a b c d EA = = 1 0 c d a b EA is the matrix which results from A by exchanging the two rows. (to be expected according to the theorem above.) Theorem 2 Every elementary matrix is invertible, and the inverse is also an elementary matrix. Theorem 3 If A is a n £ n matrix then the following statements are equivalent 1. A is invertible 2. Ax = 0 has only the trivial solution 3. The reduced echelon form of A is In 4. A can be expressed as a product of elementary matrices. 1 Proof: Prove the theorem by proving (a))(b))(c))(d))(a): (a))(b): Assume A is invertible and x0 is a solution of Ax = 0, then ¡1 Ax0 = 0, multiplying both sides with A gives ¡1 (A A)x0 = 0 , Inx0 = 0 , x0 = 0.