Implementation of Gaussian- Elimination
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Arxiv:2003.06292V1 [Math.GR] 12 Mar 2020 Eggnrtr N Ignlmti.Tedaoa Arxi Matrix Diagonal the Matrix
ALGORITHMS IN LINEAR ALGEBRAIC GROUPS SUSHIL BHUNIA, AYAN MAHALANOBIS, PRALHAD SHINDE, AND ANUPAM SINGH ABSTRACT. This paper presents some algorithms in linear algebraic groups. These algorithms solve the word problem and compute the spinor norm for orthogonal groups. This gives us an algorithmic definition of the spinor norm. We compute the double coset decompositionwith respect to a Siegel maximal parabolic subgroup, which is important in computing infinite-dimensional representations for some algebraic groups. 1. INTRODUCTION Spinor norm was first defined by Dieudonné and Kneser using Clifford algebras. Wall [21] defined the spinor norm using bilinear forms. These days, to compute the spinor norm, one uses the definition of Wall. In this paper, we develop a new definition of the spinor norm for split and twisted orthogonal groups. Our definition of the spinornorm is rich in the sense, that itis algorithmic in nature. Now one can compute spinor norm using a Gaussian elimination algorithm that we develop in this paper. This paper can be seen as an extension of our earlier work in the book chapter [3], where we described Gaussian elimination algorithms for orthogonal and symplectic groups in the context of public key cryptography. In computational group theory, one always looks for algorithms to solve the word problem. For a group G defined by a set of generators hXi = G, the problem is to write g ∈ G as a word in X: we say that this is the word problem for G (for details, see [18, Section 1.4]). Brooksbank [4] and Costi [10] developed algorithms similar to ours for classical groups over finite fields. -
Orthogonal Reduction 1 the Row Echelon Form -.: Mathematical
MATH 5330: Computational Methods of Linear Algebra Lecture 9: Orthogonal Reduction Xianyi Zeng Department of Mathematical Sciences, UTEP 1 The Row Echelon Form Our target is to solve the normal equation: AtAx = Atb ; (1.1) m×n where A 2 R is arbitrary; we have shown previously that this is equivalent to the least squares problem: min jjAx−bjj : (1.2) x2Rn t n×n As A A2R is symmetric positive semi-definite, we can try to compute the Cholesky decom- t t n×n position such that A A = L L for some lower-triangular matrix L 2 R . One problem with this approach is that we're not fully exploring our information, particularly in Cholesky decomposition we treat AtA as a single entity in ignorance of the information about A itself. t m×m In particular, the structure A A motivates us to study a factorization A=QE, where Q2R m×n is orthogonal and E 2 R is to be determined. Then we may transform the normal equation to: EtEx = EtQtb ; (1.3) t m×m where the identity Q Q = Im (the identity matrix in R ) is used. This normal equation is equivalent to the least squares problem with E: t min Ex−Q b : (1.4) x2Rn Because orthogonal transformation preserves the L2-norm, (1.2) and (1.4) are equivalent to each n other. Indeed, for any x 2 R : jjAx−bjj2 = (b−Ax)t(b−Ax) = (b−QEx)t(b−QEx) = [Q(Qtb−Ex)]t[Q(Qtb−Ex)] t t t t t t t t 2 = (Q b−Ex) Q Q(Q b−Ex) = (Q b−Ex) (Q b−Ex) = Ex−Q b : Hence the target is to find an E such that (1.3) is easier to solve. -
Naïve Gaussian Elimination Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America [email protected]
nbm_sle_sim_naivegauss.nb 1 Naïve Gaussian Elimination Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America [email protected] Introduction One of the most popular numerical techniques for solving simultaneous linear equations is Naïve Gaussian Elimination method. The approach is designed to solve a set of n equations with n unknowns, [A][X]=[C], where A nxn is a square coefficient matrix, X nx1 is the solution vector, and C nx1 is the right hand side array. Naïve Gauss consists of two steps: 1) Forward Elimination: In this step, the unknown is eliminated in each equation starting with the first@ D equation. This way, the equations @areD "reduced" to one equation and@ oneD unknown in each equation. 2) Back Substitution: In this step, starting from the last equation, each of the unknowns is found. To learn more about Naïve Gauss Elimination as well as the pitfall's of the method, click here. A simulation of Naive Gauss Method follows. Section 1: Input Data Below are the input parameters to begin the simulation. This is the only section that requires user input. Once the values are entered, Mathematica will calculate the solution vector [X]. èNumber of equations, n: n = 4 4 è nxn coefficient matrix, [A]: nbm_sle_sim_naivegauss.nb 2 A = Table 1, 10, 100, 1000 , 1, 15, 225, 3375 , 1, 20, 400, 8000 , 1, 22.5, 506.25, 11391 ;A MatrixForm 1 10@88 100 1000 < 8 < 18 15 225 3375< 8 <<D êê 1 20 400 8000 ji 1 22.5 506.25 11391 zy j z j z j z j z è nx1 rightj hand side array, [RHS]: z k { RHS = Table 227.04, 362.78, 517.35, 602.97 ; RHS MatrixForm 227.04 362.78 @8 <D êê 517.35 ji 602.97 zy j z j z j z j z j z k { Section 2: Naïve Gaussian Elimination Method The following sections divide Naïve Gauss elimination into two steps: 1) Forward Elimination 2) Back Substitution To conduct Naïve Gauss Elimination, Mathematica will join the [A] and [RHS] matrices into one augmented matrix, [C], that will facilitate the process of forward elimination. -
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 . -
Linear Programming
Linear Programming Lecture 1: Linear Algebra Review Lecture 1: Linear Algebra Review Linear Programming 1 / 24 1 Linear Algebra Review 2 Linear Algebra Review 3 Block Structured Matrices 4 Gaussian Elimination Matrices 5 Gauss-Jordan Elimination (Pivoting) Lecture 1: Linear Algebra Review Linear Programming 2 / 24 columns rows 2 3 a1• 6 a2• 7 = a a ::: a = 6 7 •1 •2 •n 6 . 7 4 . 5 am• 2 3 2 T 3 a11 a21 ::: am1 a•1 T 6 a12 a22 ::: am2 7 6 a•2 7 AT = 6 7 = 6 7 = aT aT ::: aT 6 . .. 7 6 . 7 1• 2• m• 4 . 5 4 . 5 T a1n a2n ::: amn a•n Matrices in Rm×n A 2 Rm×n 2 3 a11 a12 ::: a1n 6 a21 a22 ::: a2n 7 A = 6 7 6 . .. 7 4 . 5 am1 am2 ::: amn Lecture 1: Linear Algebra Review Linear Programming 3 / 24 rows 2 3 a1• 6 a2• 7 = 6 7 6 . 7 4 . 5 am• 2 3 2 T 3 a11 a21 ::: am1 a•1 T 6 a12 a22 ::: am2 7 6 a•2 7 AT = 6 7 = 6 7 = aT aT ::: aT 6 . .. 7 6 . 7 1• 2• m• 4 . 5 4 . 5 T a1n a2n ::: amn a•n Matrices in Rm×n A 2 Rm×n columns 2 3 a11 a12 ::: a1n 6 a21 a22 ::: a2n 7 A = 6 7 = a a ::: a 6 . .. 7 •1 •2 •n 4 . 5 am1 am2 ::: amn Lecture 1: Linear Algebra Review Linear Programming 3 / 24 2 3 2 T 3 a11 a21 ::: am1 a•1 T 6 a12 a22 ::: am2 7 6 a•2 7 AT = 6 7 = 6 7 = aT aT ::: aT 6 . -
Math 204, Spring 2020 About the First Test
Math 204, Spring 2020 About the First Test The first test for this course will be given in class on Friday, February 21. It covers all of the material that we have done in Chapters 1 and 2, up to Chapter 2, Section II. For the test, you should know and understand all the definitions and theorems that we have covered. You should be able to work with matrices and systems of linear equations. The test will include some \short essay" questions that ask you to define something, or discuss something, or explain something, and so on. Other than that, you can expect most of the questions to be similar to problems that have been given on the homework. You can expect to do a few proofs, but they will be fairly straightforward. Here are some terms and ideas that you should be familiar with for the test: systems of linear equations solution set of a linear system of equations Gaussian elimination the three row operations notations for row operations: kρi + ρj, ρi $ ρj, kρj row operations are reversible applying row operations to a system of equations echelon form for a system of linear equations matrix; rows and columns of a matrix; m × n matrix representing a system of linear equations as an augmented matrix row operations and echelon form for matrices applying row operations to a matrix n vectors in R ; column vectors and row vectors n vector addition and scalar multiplication for vectors in R n linear combination of vectors in R expressing the solution set of a linear system in vector form homogeneous system of linear equations associated homogeneous -
Gaussian Elimination and Lu Decomposition (Supplement for Ma511)
GAUSSIAN ELIMINATION AND LU DECOMPOSITION (SUPPLEMENT FOR MA511) D. ARAPURA Gaussian elimination is the go to method for all basic linear classes including this one. We go summarize the main ideas. 1. Matrix multiplication The rule for multiplying matrices is, at first glance, a little complicated. If A is m × n and B is n × p then C = AB is defined and of size m × p with entries n X cij = aikbkj k=1 The main reason for defining it this way will be explained when we talk about linear transformations later on. THEOREM 1.1. (1) Matrix multiplication is associative, i.e. A(BC) = (AB)C. (Henceforth, we will drop parentheses.) (2) If A is m × n and Im×m and In×n denote the identity matrices of the indicated sizes, AIn×n = Im×mA = A. (We usually just write I and the size is understood from context.) On the other hand, matrix multiplication is almost never commutative; in other words generally AB 6= BA when both are defined. So we have to be careful not to inadvertently use it in a calculation. Given an n × n matrix A, the inverse, if it exists, is another n × n matrix A−1 such that AA−I = I;A−1A = I A matrix is called invertible or nonsingular if A−1 exists. In practice, it is only necessary to check one of these. THEOREM 1.2. If B is n × n and AB = I or BA = I, then A invertible and B = A−1. Proof. The proof of invertibility of A will need to wait until we talk about deter- minants. -
Numerical Analysis Lecture Notes Peter J
Numerical Analysis Lecture Notes Peter J. Olver 4. Gaussian Elimination In this part, our focus will be on the most basic method for solving linear algebraic systems, known as Gaussian Elimination in honor of one of the all-time mathematical greats — the early nineteenth century German mathematician Carl Friedrich Gauss. As the father of linear algebra, his name will occur repeatedly throughout this text. Gaus- sian Elimination is quite elementary, but remains one of the most important algorithms in applied (as well as theoretical) mathematics. Our initial focus will be on the most important class of systems: those involving the same number of equations as unknowns — although we will eventually develop techniques for handling completely general linear systems. While the former typically have a unique solution, general linear systems may have either no solutions or infinitely many solutions. Since physical models require exis- tence and uniqueness of their solution, the systems arising in applications often (but not always) involve the same number of equations as unknowns. Nevertheless, the ability to confidently handle all types of linear systems is a basic prerequisite for further progress in the subject. In contemporary applications, particularly those arising in numerical solu- tions of differential equations, in signal and image processing, and elsewhere, the governing linear systems can be huge, sometimes involving millions of equations in millions of un- knowns, challenging even the most powerful supercomputer. So, a systematic and careful development of solution techniques is essential. Section 4.5 discusses some of the practical issues and limitations in computer implementations of the Gaussian Elimination method for large systems arising in applications. -
Math 304 Linear Algebra from Wednesday: I Basis and Dimension
Highlights Math 304 Linear Algebra From Wednesday: I basis and dimension I transition matrix for change of basis Harold P. Boas Today: [email protected] I row space and column space June 12, 2006 I the rank-nullity property Example Example continued 1 2 1 2 1 1 2 1 2 1 1 2 1 2 1 Let A = 2 4 3 7 1 . R2−2R1 2 4 3 7 1 −−−−−→ 0 0 1 3 −1 4 8 5 11 3 R −4R 4 8 5 11 3 3 1 0 0 1 3 −1 Three-part problem. Find a basis for 1 2 1 2 1 1 2 0 −1 2 R3−R2 R1−R2 5 −−−−→ 0 0 1 3 −1 −−−−→ 0 0 1 3 −1 I the row space (the subspace of R spanned by the rows of the matrix) 0 0 0 0 0 0 0 0 0 0 3 I the column space (the subspace of R spanned by the columns of the matrix) Notice that in each step, the second column is twice the first column; also, the second column is the sum of the third and the nullspace (the set of vectors x such that Ax = 0) I fifth columns. We can analyze all three parts by Gaussian elimination, Row operations change the column space but preserve linear even though row operations change the column space. relations among the columns. The final row-echelon form shows that the column space has dimension equal to 2, and the first and third columns are linearly independent. -
Gaussian Elimination and Matrix Inverse (Updated September 3, 2010) – 1 M
Gaussian Elimination Example 1 Gaussian elimination for the solution of a linear system transforms the Suppose that we want to solve 0 1 0 1 0 1 system Sx = f into an equivalent system Ux = c with upper triangular 2 4 −2 x1 2 matrix U (that means all entries in U below the diagonal are zero). @ 4 9 −3A @x2A = @ 8 A : (1) −2 −3 7 x 10 This transformation is done by applying three types of transformations to 3 the augmented matrix (S j f). We apply Gaussian elimination. To keep track of the operations, we use, e.g., R2 = R2 − 2 ∗ R1, which means that the new row 2 is computed by Type 1: Interchange two equations; and subtracting 2 times row 1 from row 2. Type 2: Replace an equation with the sum of the same equation and a 0 2 4 −2 2 1 0 2 4 −2 2 1 multiple of another equation. @ 4 9 −3 8 A ! @ 0 1 1 4 A R2 = R2 − 2 ∗ R1 −2 −3 7 10 0 1 5 12 R3 = R3 + R1 Once the augmented matrix (U j f) is transformed into (U j c), where U 0 2 4 −2 2 1 is an upper triangular matrix, we can solve this transformed system ! @ 0 1 1 4 A Ux = c using backsubstitution. 0 0 4 8 R3 = R3 − R2 The original system (1) is equivalent to 0 1 0 1 0 1 2 4 −2 x1 2 @0 1 1 A @x2A = @4A : (2) 0 0 4 x3 8 The system (2) can be solved by backsubstitution. -
Matrix Algebra
AN INTRODUCTION TO MATRIX ALGEBRA Craig W. Rasmussen Department of Mathematics Naval Postgraduate School AN INTRODUCTION TO MATRIX ALGEBRA Craig W. Rasmussen Department of Mathematics Naval Postgraduate School PREFACE These notes are intended to serve as the text for MA1042:Matrix Algebra, and for its 6-week refresher equivalent, MAR142. The intent of MA1042 and MAR142 is to equip students with the fundamental mechanical skills for follow-on courses in linear algebra that require a basic working knowledge of matrix algebra and complex arithmetic. In the Department of Mathematics, the principal target course is MA3042, Linear Algebra. Those whose plans lead to MA3046, Computational Linear Algebra, are urged to take MA1043 rather than MA1042, although these notes might serve as the nucleus of the material covered in that course. Other disciplines make heavy use of these techniques, as well; one must venture into the arts and humanities to find fields in which linear algebra does not come into play. The notes contain two sections that are somewhat beyond what is nor- mally covered in MA1042. The first is section 2.5, which gives a cursory introduction to vector spaces and linear transformations. Optional reading for students in MA1042, this provides a glimpse of the subject matter of MA3042. The second is section 3.5, the topic of which is the cross product. This is of potential interest to students of multivariable (three, to be precise) calculus. The course opens with an introduction to systems of linear equations and their solution by substitution. The limitations of this method quickly be- come apparent, and we begin the development of alternatives by discussing the simplicity of the substitution method on certain highly structured sys- tems. -
Gaussian Elimination Without Rounding
Gaussian elimination without rounding From László Lovász’s lecture notes (by Péter Gács) February 15, 2018 The determinant Let A = ¹ai jº be an arbitrary n × n matrix consisting of integers. Let us understand, rst of all, that the polynomial computation of det¹Aº is not inherently impossible, i.e. the result can be written with polynomially many 2 digits. Let K = max jai j j, then to write A we need obviously at least n log K bits. On the other hand, the denition of determinants gives j det¹Aºj ≤ n!Kn; hence det¹Aº can be written using log¹n!Knº + O¹1º ≤ n¹log n + log Kº + O¹1º bits. Thus, det¹Aº can be written with polynomially many bits. Linear algebra gives a formula for each element of det¹A−1º as the quotient of two subdeter- minants of A. This shows that A−1 can also be written with polynomially many digits. Exercise 1 Show that if A is a square matrix consisting of integers then to write det¹Aº we need at most as many bits as to write A. [Hint: If a1;:::; an are the row vectors of A then j det¹Aºj ≤ ja1j · · · jan j (this so-called “Hadamard-inequality” is analogous to the statement that the area of a parallelogram is smaller than the product of the lengths of its sides).] Gaussian elimination. The usual procedure to compute the determinant is the so-called Gaussian elim- ination. We can view this as the transformation of the matrix into a lower trian- gular matrix with column operations.