EG1C2 Matrix Algebra - Information Sheet

Slides:



Advertisements
Similar presentations
Chap. 3 Determinants 3.1 The Determinants of a Matrix
Advertisements

Refresher: Vector and Matrix Algebra Mike Kirkpatrick Department of Chemical Engineering FAMU-FSU College of Engineering.
P1 RJM 07/08/02EG1C2 Engineering Maths: Matrix Algebra 5 Solving Linear Equations Given the equations 3x + 4y = 10 2x + z = 7 y + 2z = 7 Solve by removing.
Linear Systems of Equations Ax = b Marco Lattuada Swiss Federal Institute of Technology - ETH Institut für Chemie und Bioingenieurwissenschaften ETH Hönggerberg/
Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues
Chapter 3 Determinants and Matrices
化工應用數學 授課教師: 郭修伯 Lecture 9 Matrices
Chapter 5 Determinants.
Linear Algebra With Applications by Otto Bretscher. Page The Determinant of any diagonal nxn matrix is the product of its diagonal entries. True.
Compiled By Raj G. Tiwari
1 February 24 Matrices 3.2 Matrices; Row reduction Standard form of a set of linear equations: Chapter 3 Linear Algebra Matrix of coefficients: Augmented.
ECON 1150 Matrix Operations Special Matrices
Eigenvalue Problems Solving linear systems Ax = b is one part of numerical linear algebra, and involves manipulating the rows of a matrix. The second main.
 Row and Reduced Row Echelon  Elementary Matrices.
Matrix Inversion.
Slide Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley.
Gaussian Elimination, Rank and Cramer
Matrix Algebra. Quick Review Quick Review Solutions.
CHAPTER 2 MATRIX. CHAPTER OUTLINE 2.1 Introduction 2.2 Types of Matrices 2.3 Determinants 2.4 The Inverse of a Square Matrix 2.5 Types of Solutions to.
Matrices & Determinants Chapter: 1 Matrices & Determinants.
8.1 Matrices & Systems of Equations
1 Ch. 4 Linear Models & Matrix Algebra Matrix algebra can be used: a. To express the system of equations in a compact manner. b. To find out whether solution.
1 MAC 2103 Module 3 Determinants. 2 Rev.F09 Learning Objectives Upon completing this module, you should be able to: 1. Determine the minor, cofactor,
Matrices CHAPTER 8.1 ~ 8.8. Ch _2 Contents  8.1 Matrix Algebra 8.1 Matrix Algebra  8.2 Systems of Linear Algebra Equations 8.2 Systems of Linear.
Algebra 3: Section 5.5 Objectives of this Section Find the Sum and Difference of Two Matrices Find Scalar Multiples of a Matrix Find the Product of Two.
Linear algebra: matrix Eigen-value Problems Eng. Hassan S. Migdadi Part 1.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 02 Chapter 2: Determinants.
Chapter 5 MATRIX ALGEBRA: DETEMINANT, REVERSE, EIGENVALUES.
Chapter 3 Determinants Linear Algebra. Ch03_2 3.1 Introduction to Determinants Definition The determinant of a 2  2 matrix A is denoted |A| and is given.
P1 RJM 06/08/02EG1C2 Engineering Maths: Matrix Algebra 1 EG1C2 Engineering Maths : Matrix Algebra Dr Richard Mitchell, Department of Cybernetics AimDescribe.
Solving Linear Systems Solving linear systems Ax = b is one part of numerical linear algebra, and involves manipulating the rows of a matrix. Solving linear.
P1 RJM 18/02/03EG1C2 Engineering Maths: Matrix Algebra Revision 7 (a) Figure Q7-1 shows an electronic circuit with two batteries and three resistors. The.
Review of Matrix Operations Vector: a sequence of elements (the order is important) e.g., x = (2, 1) denotes a vector length = sqrt(2*2+1*1) orientation.
Chapter 2 Determinants. With each square matrix it is possible to associate a real number called the determinant of the matrix. The value of this number.
P1 RJM 07/08/02EG1C2 Engineering Maths: Matrix Algebra 3 Determinants and Inverses Consider the weight suspended by wires problem: One (poor) way is to.
STROUD Worked examples and exercises are in the text Programme 5: Matrices MATRICES PROGRAMME 5.
Section 2.1 Determinants by Cofactor Expansion. THE DETERMINANT Recall from algebra, that the function f (x) = x 2 is a function from the real numbers.
STROUD Worked examples and exercises are in the text PROGRAMME 5 MATRICES.
2 - 1 Chapter 2A Matrices 2A.1 Definition, and Operations of Matrices: 1 Sums and Scalar Products; 2 Matrix Multiplication 2A.2 Properties of Matrix Operations;
Linear Algebra Engineering Mathematics-I. Linear Systems in Two Unknowns Engineering Mathematics-I.
Matrices, Vectors, Determinants.
Linear Algebra With Applications by Otto Bretscher.
Matrices Introduction.
2. Matrix Methods
Sec 3.6 Determinants 2x2 matrix Evaluate the determinant of.
nhaa/imk/sem /eqt101/rk12/32
Introduction The central problems of Linear Algebra are to study the properties of matrices and to investigate the solutions of systems of linear equations.
7.7 Determinants. Cramer’s Rule
Lecture 2 Matrices Lat Time - Course Overview
Warm-up Problem Use the Laplace transform to solve the IVP.
Review of Matrix Operations
Elementary Linear Algebra Anton & Rorres, 9th Edition
LINEAR ALGEBRA.
ECE 3301 General Electrical Engineering
CHAPTER 2 MATRICES Determinant Inverse.
Linear Algebra Lecture 19.
Linear Algebra Lecture 36.
Chapter 2 Determinants by Cofactor Expansion
Systems of First Order Linear Equations
DETERMINANT MATRIX YULVI ZAIKA.
Vector and Matrix Material
Review of Matrix Algebra
Chapter 3 Linear Algebra
Elementary Linear Algebra Anton & Rorres, 9th Edition
DETERMINANT MATH 80 - Linear Algebra.
nhaa/imk/sem /eqt101/rk12/32
Chapter 2 Determinants.
Linear Algebra: Matrix Eigenvalue Problems – Part 2
Chapter 2 Determinants.
Matrices - Operations ADJOINT MATRICES
Presentation transcript:

EG1C2 Matrix Algebra - Information Sheet Note, information such as that on this sheet is provided for students when they do their tests and in their Part 1 exam. Matrix Definitions aij is element in row i column j of A p1 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

EG1C2 Engineering Maths: Matrix Algebra Data Sheet Determinants Determinant of A, detA = |A| = |a11| = a11 Determinant of a matrix A omitting ith row and jth column is Mij The cofactor of element i,j, Cij, is (-1)i+jMij |A| = a11*C11 + a12*C12 + .. + a1n*C1n p2 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

Adjoint, Inverse and Transpose If A is square, the Adjoint matrix of A, Adj(A), = [Cji] = [Cij]T (A-1)-1 = A A * A-1 = A-1 * A = I (A * B)-1 = B-1 * A-1 AT is the transpose of matrix A. (AT)T=A (A+B)T=AT+BT (A*B)T=BT*AT A is a symmetrix matrix if AT=A. A is a skew-symmetrix matrix, if AT=-A If AT=A-1 then A is an orthogonal matrix. p3 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

Gaussian Elimination: to solve for x in A x = b Then process until A part is triangular, using row operations row X := p * row Y + q * row Z p4 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

EG1C2 Engineering Maths: Matrix Algebra Data Sheet Gauss Jordan To invert matrix A Then do row operations until left half = I, A-1 is right half p5 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

EG1C2 Engineering Maths: Matrix Algebra Data Sheet Cramer’s Theorem A linear system, described by equation A x = b, has solutions: x1 = D1/D x2 = D2/D ... xm = Dm/D where D is |A| and Dk is determinant of A when kth column replaced by b. Rank of m*n matrix Is the largest square submatrix whose determinant is non zero. A submatrix is a matrix with any row and/or column removed. p6 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

Transformation Matrices For transforming point x,y to point x’,y’ If T is product of such matrices, x,y transformed to x’,y’ by p7 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

Eigenvalues and Eigenvectors Suppose A is an n*n matrix, l a scalar and x an n element vector. An eigenvalue of A is any value for l which is a solution to the equation A*x - l*x = 0 for which x <> 0. The n eigenvalues are found by solving |A - l*I| = 0. The n eigenvectors are the n vectors which satisfy (A - l*I) x = 0 for each of the n values of l. State Space Equations p8 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

State Space Solutions, for A is 2*2 If A has 2 real non repeating eigenvalues l1 and l2 and eigenvalues L1 and L2, general solution is x = c1*L1*el1t + c2*L2*el2t (c1 and c2 are constants). If eigenvalues are complex, l1, l2 = a  jb, and eigenvectors are L1, L2 = A  jB, then the response is (for constants ca and cb): x = eat { ca A (cos (bt) + sin (bt) ) + cb B (cos (bt) – sin(bt) ) } {Equivalent to the above expression: ca=c1+c2 and cb=j(c1-c2)} If A has repeated eigenvalue l and eigenvector  the response is x = c1*L*elt + c2*(t L+V) *elt where V is the solution of (A - lI)V =  p9 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

EG1C2 Engineering Maths: Matrix Algebra Data Sheet Two Port Networks Can be defined by: ‘A’ matrices for the following simple elements: If two elements in series are described by matrices A1 and A2, the matrix describing the elements combined is A1 * A2 p10 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

EG1C2 Engineering Maths: Matrix Algebra Data Sheet These networks can also be defined by the ‘Z’, ‘Y’ or ‘H’ matrices p11 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet

EG1C2 Engineering Maths: Matrix Algebra Data Sheet Table to convert between these two port network matrices p12 RJM 08/08/02 EG1C2 Engineering Maths: Matrix Algebra Data Sheet