New Method to Calculate Determinants of n n(n 3) Matrix, by Reducing Determinants to 2nd Order
|
|
|
- Laurel Hutchinson
- 9 years ago
- Views:
Transcription
1 International Journal of Algebra, Vol 6, 202, no 9, New Method to Calculate Determinants of n n(n 3) Matrix, by Reducing Determinants to 2nd Order Armend Salihu Department of Telecommunication, Faculty of Electrical and Computer Engineering, University of Prishtina, Bregu i Diellit pn, 0000 Prishtina, Kosovo arsalihu@gmailcom Abstract In this paper we will present a new method to calculate of n n (n 3) order determinants This method is based on Dodgson - Chio s condensation method, but the priority of this method compared with Dodgson - Chio s and minors method as well is that those method decreases the order of determinants for one, and this new method automatically affects in reducing the order of determinants in 2nd order Mathematics Subject Classification: 65F40, C20, 5A5 Keywords: New method to calculate determinants of n n matrix Introduction Let A be an n n matrix a a 2 a n a 2 a 22 a 2n A a n a n2 a nn Definition A determinant of order n, or size n n, (see [2], [3], [7], [8]) is the sum a a 2 a n a 2 a 22 a 2n D det(a) A ε j,j 2,,j n a j a j2 a jn, S n a n a n2 a nn ranging over the symmetric permutation group S n, where
2 94 A Salihu ε j,j 2,,j n { +, if j,j 2,,j n is an even permutation, if j,j 2,,j n is an odd permuation Chio s condensation method Chio s condensation is a method for evaluating an n n determinant in terms of (n ) (n ) determinants; see [4], [5]: A a a 2 a n a 2 a 22 a 2n a n a n2 a nn a n 2 a a 2 a 2 a 22 a a 2 a 3 a 32 a a 2 a n a n2 a a 3 a 2 a 23 a a 3 a 3 a 33 a a 3 a n a n3 a a 2 a a 3 a a n a n a 2n a n a 3n a n a nn 2 Dodgson s condensation method Dodgson s condensation method computes determinants of size n n by expressing them in terms of those of size (n ) (n ), and then expresses the latter in terms of determinants of size (n 2) (n 2), and so on (see [6]) 2 A new method This method is based on Dodgson and Chio s method, but the diference between them is that this new method is resolved by calculating 4 unique determinants of (n ) (n ) Order, (which can be derived from determinants of n n order, if we remove first row and first column or first row and last column or last row and first column or last row and last column, elements that belongs to only one of unique determinants we should call them unique elements), and one determinant of (n 2) (n 2) order which is formed from n n order determinant with elements a i,j with i, j,n, on condition that the determinant of (n 2) (n 2) 0 Theorem : Every determinant of n n (n > 2) order can be reduced into 2 2 order determinant, by calculating 4 determinants of (n ) (n ) order, and one determinant of (n 2) (n 2) order, on condition that (n 2) (n 2) order determinants to be different from zero Ongoing is presented a scheme of calculating the determinants of n n order according to this formula:
3 Method to compute n n determinant 95 a a 2 a n a 2 a 22 a 2n A a n a n2 a nn B C D E F, B 0 The B is (n 2) (n 2) order determinant which is the interior determinant of determinant A while C, D, E and F are unique determinants of (n ) (n ) order, which can be formed from n n order determinant Proof: Lets be n 4, and we will prove that the same result we can achieve when we calculate this determinant according to the above scheme: A a a 2 a 3 a 4 a 2 a 3 a 4 a 22 a 23 a 23 a 33 a a 2 a 3 a 2 a 22 a 23 a 3 a 32 a 33 a 2 a 22 a 23 a 3 a 32 a 33 a 4 a 42 a 43 a 2 a 3 a 4 a a 2 a 3 a 2 a 22 a 23 a 3 a 32 a 33 a 22 a 33 a 23 a 22 a 2 a 22 a 23 a 3 a 32 a 33 a 4 a 42 a 43 a 22 a 33 a 23 a 22 (A A 2 ) A A 2 (a 22 a 33 a 23 a 32 )(a a 22 a 33 a 44 +a a 23 a 34 a 42 +a a 24 a 32 a 43 a a 22 a 34 a 43 a a 23 a 32 a 44 a a 24 a 33 a 42 +a 2 a 23 a 3 a 44 +a 3 a 2 a 32 a 44 a 3 a 22 a 3 a 44 a 2 a 2 a 33 a a 2 a 2 a 34 a 43 +a 3 a 3 a 24 a 42 a 4 a 2 a 32 a 43 a 2 a 23 a 34 a 4 a 3 a 24 a 32 a 4 a 4 a 22 a 33 a 4 + +a 2 a 24 a 33 a 4 +a 3 a 22 a 34 a 4 +a 4 a 23 a 32 a 4 a 4 a 23 a 3 a 42 +a 4 a 2 a 33 a 42 +a 4 a 22 a 3 a 43 a 2 a 24 a 3 a 43 a 3 a 2 a 34 a 42 ) A a 22 a 33 a 23 a 32 (A A 2 ) a 22 a 33 a 23 a 32 (a 22 a 33 a 23 a 32 )
4 96 A Salihu (a a 22 a 33 a 44 +a a 23 a 34 a 42 +a a 24 a 32 a 43 a a 22 a 34 a 43 a a 23 a 32 a 44 a a 24 a 33 a a 2 a 23 a 3 a 44 +a 3 a 2 a 32 a 44 a 3 a 22 a 3 a 44 a 2 a 2 a 33 a 44 +a 2 a 2 a 34 a 43 +a 3 a 3 a 24 a 42 a 4 a 2 a 32 a 43 a 2 a 23 a 34 a 4 a 3 a 24 a 32 a 4 a 4 a 22 a 33 a 4 +a 2 a 24 a 33 a 4 +a 3 a 22 a 34 a 4 + +a 4 a 23 a 32 a 4 a 4 a 23 a 3 a 42 +a 4 a 2 a 33 a 42 +a 4 a 22 a 3 a 43 a 2 a 24 a 3 a 43 a 3 a 2 a 34 a 42 ) a a 2 a 3 a 4 a 2 a 3 a 4 Based on this we can outcome to the result: all combinations from C D E F which does not contain one of ε j,j 2,,j n a j a j2 a jn combinations from B determinant, and does not contain one of unique elements, as a result of crossed multiplication, they should be eliminated between each other, while other combinations which contain one of ε j,j 2,,j n a j a j2 a jn combinations from B determinants, extract as common elements and after divided by determinant B we get the result of the given determinant Example: let be the 5 order determinant: A ( ) The same result we can achieve even by calculating this determinant in other methods
5 Method to compute n n determinant 97 References [] C L Dodgson, Condensation of Determinants, Being a New and Brief Method for Computing their Arithmetic Values, Proc Roy Soc Ser A, 5(866), [2] D Hajrizaj, New method to compute determinant of a 3x3 matrix, International Journal of Algebra, Vol 3, 2009, no 5, 2-29 [3] E Hamiti, Matematika, Universiteti i Prishtines: Fakulteti Elektroteknik, Prishtine, (2000), [4] F Chió, Mémoire sur les fonctions connues sous le nom de résultantes ou de déterminants Turin: E Pons, 853 [5] H Eves, An Introduction to the History of Mathematics, pages 405 and 493, Saunders College Publishing, 990 [6] H Eves, Chio s Expansion, 36 in Elementary Matrix Theory, New York: Dover, (996), [7] http : //enwikipediaorg/wiki/dodgson condensation [8] Q Gjonbalaj, ASalihu, Computing the determinants by reducing the order by four, Applied Mathematics E-Notes, 0(200), 5-58 [9] R F Scott, The theory of determinants and their applications, Ithaca, New York: Cornell University Library, Cambridge: University Press, (904), 3-5 [0] S Barnard and J M Child, Higher Algebra, London Macmillan LTD New York, ST Martin s Press (959), 3 [] W L Ferrar, Algebra, A Text-Book of Determinants, Matrices, and Algebraic Forms, Second edition, Fellow and tutor of Hertford College Oxford, (957), 7 Received: April, 202
The Determinant: a Means to Calculate Volume
The Determinant: a Means to Calculate Volume Bo Peng August 20, 2007 Abstract This paper gives a definition of the determinant and lists many of its well-known properties Volumes of parallelepipeds are
Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = 36 + 41i.
Math 5A HW4 Solutions September 5, 202 University of California, Los Angeles Problem 4..3b Calculate the determinant, 5 2i 6 + 4i 3 + i 7i Solution: The textbook s instructions give us, (5 2i)7i (6 + 4i)(
Notes on Determinant
ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without
A Direct Numerical Method for Observability Analysis
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 15, NO 2, MAY 2000 625 A Direct Numerical Method for Observability Analysis Bei Gou and Ali Abur, Senior Member, IEEE Abstract This paper presents an algebraic method
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
The Characteristic Polynomial
Physics 116A Winter 2011 The Characteristic Polynomial 1 Coefficients of the characteristic polynomial Consider the eigenvalue problem for an n n matrix A, A v = λ v, v 0 (1) The solution to this problem
Two classes of ternary codes and their weight distributions
Two classes of ternary codes and their weight distributions Cunsheng Ding, Torleiv Kløve, and Francesco Sica Abstract In this paper we describe two classes of ternary codes, determine their minimum weight
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
Unit 18 Determinants
Unit 18 Determinants Every square matrix has a number associated with it, called its determinant. In this section, we determine how to calculate this number, and also look at some of the properties of
Continued Fractions and the Euclidean Algorithm
Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction
Matrix Algebra. Some Basic Matrix Laws. Before reading the text or the following notes glance at the following list of basic matrix algebra laws.
Matrix Algebra A. Doerr Before reading the text or the following notes glance at the following list of basic matrix algebra laws. Some Basic Matrix Laws Assume the orders of the matrices are such that
MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.
MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix. Nullspace Let A = (a ij ) be an m n matrix. Definition. The nullspace of the matrix A, denoted N(A), is the set of all n-dimensional column
Similarity and Diagonalization. Similar Matrices
MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that
a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.
Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given
Determinants in the Kronecker product of matrices: The incidence matrix of a complete graph
FPSAC 2009 DMTCS proc (subm), by the authors, 1 10 Determinants in the Kronecker product of matrices: The incidence matrix of a complete graph Christopher R H Hanusa 1 and Thomas Zaslavsky 2 1 Department
Recall that two vectors in are perpendicular or orthogonal provided that their dot
Orthogonal Complements and Projections Recall that two vectors in are perpendicular or orthogonal provided that their dot product vanishes That is, if and only if Example 1 The vectors in are orthogonal
SOLVING LINEAR SYSTEMS
SOLVING LINEAR SYSTEMS Linear systems Ax = b occur widely in applied mathematics They occur as direct formulations of real world problems; but more often, they occur as a part of the numerical analysis
DETERMINANTS IN THE KRONECKER PRODUCT OF MATRICES: THE INCIDENCE MATRIX OF A COMPLETE GRAPH
DETERMINANTS IN THE KRONECKER PRODUCT OF MATRICES: THE INCIDENCE MATRIX OF A COMPLETE GRAPH CHRISTOPHER RH HANUSA AND THOMAS ZASLAVSKY Abstract We investigate the least common multiple of all subdeterminants,
Using row reduction to calculate the inverse and the determinant of a square matrix
Using row reduction to calculate the inverse and the determinant of a square matrix Notes for MATH 0290 Honors by Prof. Anna Vainchtein 1 Inverse of a square matrix An n n square matrix A is called invertible
The Assignment Problem and the Hungarian Method
The Assignment Problem and the Hungarian Method 1 Example 1: You work as a sales manager for a toy manufacturer, and you currently have three salespeople on the road meeting buyers. Your salespeople are
Lecture 5 Principal Minors and the Hessian
Lecture 5 Principal Minors and the Hessian Eivind Eriksen BI Norwegian School of Management Department of Economics October 01, 2010 Eivind Eriksen (BI Dept of Economics) Lecture 5 Principal Minors and
ON GENERALIZED RELATIVE COMMUTATIVITY DEGREE OF A FINITE GROUP. A. K. Das and R. K. Nath
International Electronic Journal of Algebra Volume 7 (2010) 140-151 ON GENERALIZED RELATIVE COMMUTATIVITY DEGREE OF A FINITE GROUP A. K. Das and R. K. Nath Received: 12 October 2009; Revised: 15 December
8 Square matrices continued: Determinants
8 Square matrices continued: Determinants 8. Introduction Determinants give us important information about square matrices, and, as we ll soon see, are essential for the computation of eigenvalues. You
α = u v. In other words, Orthogonal Projection
Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v
MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.
MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column
Suk-Geun Hwang and Jin-Woo Park
Bull. Korean Math. Soc. 43 (2006), No. 3, pp. 471 478 A NOTE ON PARTIAL SIGN-SOLVABILITY Suk-Geun Hwang and Jin-Woo Park Abstract. In this paper we prove that if Ax = b is a partial signsolvable linear
26. Determinants I. 1. Prehistory
26. Determinants I 26.1 Prehistory 26.2 Definitions 26.3 Uniqueness and other properties 26.4 Existence Both as a careful review of a more pedestrian viewpoint, and as a transition to a coordinate-independent
1 Determinants and the Solvability of Linear Systems
1 Determinants and the Solvability of Linear Systems In the last section we learned how to use Gaussian elimination to solve linear systems of n equations in n unknowns The section completely side-stepped
Solving Systems of Linear Equations Using Matrices
Solving Systems of Linear Equations Using Matrices What is a Matrix? A matrix is a compact grid or array of numbers. It can be created from a system of equations and used to solve the system of equations.
FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM
International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 34-48 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT
Zachary Monaco Georgia College Olympic Coloring: Go For The Gold
Zachary Monaco Georgia College Olympic Coloring: Go For The Gold Coloring the vertices or edges of a graph leads to a variety of interesting applications in graph theory These applications include various
A note on companion matrices
Linear Algebra and its Applications 372 (2003) 325 33 www.elsevier.com/locate/laa A note on companion matrices Miroslav Fiedler Academy of Sciences of the Czech Republic Institute of Computer Science Pod
University of Lille I PC first year list of exercises n 7. Review
University of Lille I PC first year list of exercises n 7 Review Exercise Solve the following systems in 4 different ways (by substitution, by the Gauss method, by inverting the matrix of coefficients
DATA ANALYSIS II. Matrix Algorithms
DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where
Row Echelon Form and Reduced Row Echelon Form
These notes closely follow the presentation of the material given in David C Lay s textbook Linear Algebra and its Applications (3rd edition) These notes are intended primarily for in-class presentation
Operation Count; Numerical Linear Algebra
10 Operation Count; Numerical Linear Algebra 10.1 Introduction Many computations are limited simply by the sheer number of required additions, multiplications, or function evaluations. If floating-point
10.2 ITERATIVE METHODS FOR SOLVING LINEAR SYSTEMS. The Jacobi Method
578 CHAPTER 1 NUMERICAL METHODS 1. ITERATIVE METHODS FOR SOLVING LINEAR SYSTEMS As a numerical technique, Gaussian elimination is rather unusual because it is direct. That is, a solution is obtained after
A characterization of trace zero symmetric nonnegative 5x5 matrices
A characterization of trace zero symmetric nonnegative 5x5 matrices Oren Spector June 1, 009 Abstract The problem of determining necessary and sufficient conditions for a set of real numbers to be the
The four [10,5,4] binary codes
1 Preliminaries The four [10,,] binary codes There are four distinct [10; ; ] binary codes. We shall prove this in a moderately elementary way, using the MacWilliams identities as the main tool. (For the
Solution to Homework 2
Solution to Homework 2 Olena Bormashenko September 23, 2011 Section 1.4: 1(a)(b)(i)(k), 4, 5, 14; Section 1.5: 1(a)(b)(c)(d)(e)(n), 2(a)(c), 13, 16, 17, 18, 27 Section 1.4 1. Compute the following, if
Math 312 Homework 1 Solutions
Math 31 Homework 1 Solutions Last modified: July 15, 01 This homework is due on Thursday, July 1th, 01 at 1:10pm Please turn it in during class, or in my mailbox in the main math office (next to 4W1) Please
9.2 Summation Notation
9. Summation Notation 66 9. Summation Notation In the previous section, we introduced sequences and now we shall present notation and theorems concerning the sum of terms of a sequence. We begin with a
Eigenvalues and Eigenvectors
Chapter 6 Eigenvalues and Eigenvectors 6. Introduction to Eigenvalues Linear equations Ax D b come from steady state problems. Eigenvalues have their greatest importance in dynamic problems. The solution
GENERATING SETS KEITH CONRAD
GENERATING SETS KEITH CONRAD 1 Introduction In R n, every vector can be written as a unique linear combination of the standard basis e 1,, e n A notion weaker than a basis is a spanning set: a set of vectors
Solution of Linear Systems
Chapter 3 Solution of Linear Systems In this chapter we study algorithms for possibly the most commonly occurring problem in scientific computing, the solution of linear systems of equations. We start
THE SIGN OF A PERMUTATION
THE SIGN OF A PERMUTATION KEITH CONRAD 1. Introduction Throughout this discussion, n 2. Any cycle in S n is a product of transpositions: the identity (1) is (12)(12), and a k-cycle with k 2 can be written
6. Cholesky factorization
6. Cholesky factorization EE103 (Fall 2011-12) triangular matrices forward and backward substitution the Cholesky factorization solving Ax = b with A positive definite inverse of a positive definite matrix
Domino Tableaux, Schützenberger Involution, and the Symmetric Group Action
Domino Tableaux, Schützenberger Involution, and the Symmetric Group Action Arkady Berenstein and Anatol N. Kirillov CRM-2510 September 1997 Department of Mathematics, Cornell University, Ithaca, NY 14853,
LINEAR ALGEBRA. September 23, 2010
LINEAR ALGEBRA September 3, 00 Contents 0. LU-decomposition.................................... 0. Inverses and Transposes................................. 0.3 Column Spaces and NullSpaces.............................
Lecture 1: Systems of Linear Equations
MTH Elementary Matrix Algebra Professor Chao Huang Department of Mathematics and Statistics Wright State University Lecture 1 Systems of Linear Equations ² Systems of two linear equations with two variables
Continuity of the Perron Root
Linear and Multilinear Algebra http://dx.doi.org/10.1080/03081087.2014.934233 ArXiv: 1407.7564 (http://arxiv.org/abs/1407.7564) Continuity of the Perron Root Carl D. Meyer Department of Mathematics, North
MAT188H1S Lec0101 Burbulla
Winter 206 Linear Transformations A linear transformation T : R m R n is a function that takes vectors in R m to vectors in R n such that and T (u + v) T (u) + T (v) T (k v) k T (v), for all vectors u
= 2 + 1 2 2 = 3 4, Now assume that P (k) is true for some fixed k 2. This means that
Instructions. Answer each of the questions on your own paper, and be sure to show your work so that partial credit can be adequately assessed. Credit will not be given for answers (even correct ones) without
A permutation can also be represented by describing its cycles. What do you suppose is meant by this?
Shuffling, Cycles, and Matrices Warm up problem. Eight people stand in a line. From left to right their positions are numbered,,,... 8. The eight people then change places according to THE RULE which directs
160 CHAPTER 4. VECTOR SPACES
160 CHAPTER 4. VECTOR SPACES 4. Rank and Nullity In this section, we look at relationships between the row space, column space, null space of a matrix and its transpose. We will derive fundamental results
Mathematics Course 111: Algebra I Part IV: Vector Spaces
Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are
13 MATH FACTS 101. 2 a = 1. 7. The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.
3 MATH FACTS 0 3 MATH FACTS 3. Vectors 3.. Definition We use the overhead arrow to denote a column vector, i.e., a linear segment with a direction. For example, in three-space, we write a vector in terms
v w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors.
3. Cross product Definition 3.1. Let v and w be two vectors in R 3. The cross product of v and w, denoted v w, is the vector defined as follows: the length of v w is the area of the parallelogram with
1.2. Successive Differences
1. An Application of Inductive Reasoning: Number Patterns In the previous section we introduced inductive reasoning, and we showed how it can be applied in predicting what comes next in a list of numbers
Classification of Cartan matrices
Chapter 7 Classification of Cartan matrices In this chapter we describe a classification of generalised Cartan matrices This classification can be compared as the rough classification of varieties in terms
Matrices 2. Solving Square Systems of Linear Equations; Inverse Matrices
Matrices 2. Solving Square Systems of Linear Equations; Inverse Matrices Solving square systems of linear equations; inverse matrices. Linear algebra is essentially about solving systems of linear equations,
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation
Observation on Sums of Powers of Integers Divisible by Four
Applied Mathematical Sciences, Vol. 8, 2014, no. 45, 2219-2226 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.4140 Observation on Sums of Powers of Integers Divisible by Four Djoko Suprijanto
Algebraic Concepts Algebraic Concepts Writing
Curriculum Guide: Algebra 2/Trig (AR) 2 nd Quarter 8/7/2013 2 nd Quarter, Grade 9-12 GRADE 9-12 Unit of Study: Matrices Resources: Textbook: Algebra 2 (Holt, Rinehart & Winston), Ch. 4 Length of Study:
Algebraic and Transcendental Numbers
Pondicherry University July 2000 Algebraic and Transcendental Numbers Stéphane Fischler This text is meant to be an introduction to algebraic and transcendental numbers. For a detailed (though elementary)
Elementary Matrices and The LU Factorization
lementary Matrices and The LU Factorization Definition: ny matrix obtained by performing a single elementary row operation (RO) on the identity (unit) matrix is called an elementary matrix. There are three
Orthogonal Diagonalization of Symmetric Matrices
MATH10212 Linear Algebra Brief lecture notes 57 Gram Schmidt Process enables us to find an orthogonal basis of a subspace. Let u 1,..., u k be a basis of a subspace V of R n. We begin the process of finding
Arithmetic and Algebra of Matrices
Arithmetic and Algebra of Matrices Math 572: Algebra for Middle School Teachers The University of Montana 1 The Real Numbers 2 Classroom Connection: Systems of Linear Equations 3 Rational Numbers 4 Irrational
1.2 Solving a System of Linear Equations
1.. SOLVING A SYSTEM OF LINEAR EQUATIONS 1. Solving a System of Linear Equations 1..1 Simple Systems - Basic De nitions As noticed above, the general form of a linear system of m equations in n variables
Department of Chemical Engineering ChE-101: Approaches to Chemical Engineering Problem Solving MATLAB Tutorial VI
Department of Chemical Engineering ChE-101: Approaches to Chemical Engineering Problem Solving MATLAB Tutorial VI Solving a System of Linear Algebraic Equations (last updated 5/19/05 by GGB) Objectives:
CITY UNIVERSITY LONDON. BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION
No: CITY UNIVERSITY LONDON BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION ENGINEERING MATHEMATICS 2 (resit) EX2005 Date: August
1 A duality between descents and connectivity.
The Descent Set and Connectivity Set of a Permutation 1 Richard P. Stanley 2 Department of Mathematics, Massachusetts Institute of Technology Cambridge, MA 02139, USA [email protected] version of 16 August
x1 x 2 x 3 y 1 y 2 y 3 x 1 y 2 x 2 y 1 0.
Cross product 1 Chapter 7 Cross product We are getting ready to study integration in several variables. Until now we have been doing only differential calculus. One outcome of this study will be our ability
Precalculus REVERSE CORRELATION. Content Expectations for. Precalculus. Michigan CONTENT EXPECTATIONS FOR PRECALCULUS CHAPTER/LESSON TITLES
Content Expectations for Precalculus Michigan Precalculus 2011 REVERSE CORRELATION CHAPTER/LESSON TITLES Chapter 0 Preparing for Precalculus 0-1 Sets There are no state-mandated Precalculus 0-2 Operations
Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix multiplication).
MAT 2 (Badger, Spring 202) LU Factorization Selected Notes September 2, 202 Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix
Linear Algebra Notes
Linear Algebra Notes Chapter 19 KERNEL AND IMAGE OF A MATRIX Take an n m matrix a 11 a 12 a 1m a 21 a 22 a 2m a n1 a n2 a nm and think of it as a function A : R m R n The kernel of A is defined as Note
Chapter 7. Permutation Groups
Chapter 7 Permutation Groups () We started the study of groups by considering planar isometries In the previous chapter, we learnt that finite groups of planar isometries can only be cyclic or dihedral
SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH
31 Kragujevac J. Math. 25 (2003) 31 49. SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH Kinkar Ch. Das Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, W.B.,
Direct Methods for Solving Linear Systems. Matrix Factorization
Direct Methods for Solving Linear Systems Matrix Factorization Numerical Analysis (9th Edition) R L Burden & J D Faires Beamer Presentation Slides prepared by John Carroll Dublin City University c 2011
ON THE COEFFICIENTS OF THE LINKING POLYNOMIAL
ADSS, Volume 3, Number 3, 2013, Pages 45-56 2013 Aditi International ON THE COEFFICIENTS OF THE LINKING POLYNOMIAL KOKO KALAMBAY KAYIBI Abstract Let i j T( M; = tijx y be the Tutte polynomial of the matroid
0 0 such that f x L whenever x a
Epsilon-Delta Definition of the Limit Few statements in elementary mathematics appear as cryptic as the one defining the limit of a function f() at the point = a, 0 0 such that f L whenever a Translation:
Chapter 6. Orthogonality
6.3 Orthogonal Matrices 1 Chapter 6. Orthogonality 6.3 Orthogonal Matrices Definition 6.4. An n n matrix A is orthogonal if A T A = I. Note. We will see that the columns of an orthogonal matrix must be
Al-Jo anee Company: support department cost allocations with matrices to improve decision making
Al-Jo anee Company: support department cost allocations with matrices to improve decision making ABSTRACT Saad S. Hussein University of Tikrit, Iraq Dennis F. Togo University of New Mexico, USA The direct,
1 Sets and Set Notation.
LINEAR ALGEBRA MATH 27.6 SPRING 23 (COHEN) LECTURE NOTES Sets and Set Notation. Definition (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most
SF2940: Probability theory Lecture 8: Multivariate Normal Distribution
SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2015 Timo Koski Matematisk statistik 24.09.2015 1 / 1 Learning outcomes Random vectors, mean vector, covariance matrix,
Sample Induction Proofs
Math 3 Worksheet: Induction Proofs III, Sample Proofs A.J. Hildebrand Sample Induction Proofs Below are model solutions to some of the practice problems on the induction worksheets. The solutions given
Manipulability of the Price Mechanism for Data Centers
Manipulability of the Price Mechanism for Data Centers Greg Bodwin 1, Eric Friedman 2,3,4, and Scott Shenker 3,4 1 Department of Computer Science, Tufts University, Medford, Massachusetts 02155 2 School
4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION
4: EIGENVALUES, EIGENVECTORS, DIAGONALIZATION STEVEN HEILMAN Contents 1. Review 1 2. Diagonal Matrices 1 3. Eigenvectors and Eigenvalues 2 4. Characteristic Polynomial 4 5. Diagonalizability 6 6. Appendix:
UNCOUPLING THE PERRON EIGENVECTOR PROBLEM
UNCOUPLING THE PERRON EIGENVECTOR PROBLEM Carl D Meyer INTRODUCTION Foranonnegative irreducible matrix m m with spectral radius ρ,afundamental problem concerns the determination of the unique normalized
15.062 Data Mining: Algorithms and Applications Matrix Math Review
.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop
Similar matrices and Jordan form
Similar matrices and Jordan form We ve nearly covered the entire heart of linear algebra once we ve finished singular value decompositions we ll have seen all the most central topics. A T A is positive
On the representability of the bi-uniform matroid
On the representability of the bi-uniform matroid Simeon Ball, Carles Padró, Zsuzsa Weiner and Chaoping Xing August 3, 2012 Abstract Every bi-uniform matroid is representable over all sufficiently large
A case study of social network analysis of the discussion area of a virtual learning platform
World Transactions on Engineering and Technology Education Vol.12, No.3, 2014 2014 WIETE A case study of social network analysis of the discussion area of a virtual learning platform Meimei Wu & Xinmin
Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.
Chapter 7 Eigenvalues and Eigenvectors In this last chapter of our exploration of Linear Algebra we will revisit eigenvalues and eigenvectors of matrices, concepts that were already introduced in Geometry
SOLVING POLYNOMIAL EQUATIONS BY RADICALS
SOLVING POLYNOMIAL EQUATIONS BY RADICALS Lee Si Ying 1 and Zhang De-Qi 2 1 Raffles Girls School (Secondary), 20 Anderson Road, Singapore 259978 2 Department of Mathematics, National University of Singapore,
Linear Algebra Notes for Marsden and Tromba Vector Calculus
Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of
Matrix Differentiation
1 Introduction Matrix Differentiation ( and some other stuff ) Randal J. Barnes Department of Civil Engineering, University of Minnesota Minneapolis, Minnesota, USA Throughout this presentation I have
Recall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.
ORTHOGONAL MATRICES Informally, an orthogonal n n matrix is the n-dimensional analogue of the rotation matrices R θ in R 2. When does a linear transformation of R 3 (or R n ) deserve to be called a rotation?
