(I.C) Matrix algebra

Similar documents
(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:

CHAPTER I: Vector Spaces

Matrix Algebra 2.2 THE INVERSE OF A MATRIX Pearson Education, Inc.

THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS

Chimica Inorganica 3

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

Theorem: Let A n n. In this case that A does reduce to I, we search for A 1 as the solution matrix X to the matrix equation A X = I i.e.

Matrices and vectors

Example 1.1 Use an augmented matrix to mimic the elimination method for solving the following linear system of equations.

(3) If you replace row i of A by its sum with a multiple of another row, then the determinant is unchanged! Expand across the i th row:

, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx)

B = B is a 3 4 matrix; b 32 = 3 and b 2 4 = 3. Scalar Multiplication

Determinants of order 2 and 3 were defined in Chapter 2 by the formulae (5.1)

1 Last time: similar and diagonalizable matrices

Chapter Vectors

Mon Feb matrix inverses. Announcements: Warm-up Exercise:

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

Chapter Unary Matrix Operations

Math 4707 Spring 2018 (Darij Grinberg): homework set 4 page 1

INTRODUCTION TO MATRIX ALGEBRA. a 11 a a 1n a 21 a a 2n...

Homework 2 January 19, 2006 Math 522. Direction: This homework is due on January 26, In order to receive full credit, answer

Eigenvalues and Eigenvectors

M 340L CS Homew ork Set 6 Solutions

M 340L CS Homew ork Set 6 Solutions

Chapter 2. Periodic points of toral. automorphisms. 2.1 General introduction

Matrix Algebra from a Statistician s Perspective BIOS 524/ Scalar multiple: ka

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

MATH10212 Linear Algebra B Proof Problems

The Discrete Fourier Transform

24 MATH 101B: ALGEBRA II, PART D: REPRESENTATIONS OF GROUPS

APPENDIX F Complex Numbers

15.083J/6.859J Integer Optimization. Lecture 3: Methods to enhance formulations

Lecture 8: October 20, Applications of SVD: least squares approximation

LINEAR ALGEBRA. Paul Dawkins

LECTURE NOTES, 11/10/04

4. Determinants. det : { square matrices } F less important in mordern & practical applications but in theory

Math 140A Elementary Analysis Homework Questions 1

Stochastic Matrices in a Finite Field

Chapter 1 Simple Linear Regression (part 6: matrix version)

(VII.A) Review of Orthogonality

The inverse eigenvalue problem for symmetric doubly stochastic matrices

1. By using truth tables prove that, for all statements P and Q, the statement

LECTURE 8: ORTHOGONALITY (CHAPTER 5 IN THE BOOK)

Principle Of Superposition

( ) ( ) ( ) notation: [ ]

Math 4400/6400 Homework #7 solutions

Vector Spaces and Vector Subspaces. Remarks. Euclidean Space

1. ARITHMETIC OPERATIONS IN OBSERVER'S MATHEMATICS

C191 - Lecture 2 - Quantum states and observables

Second day August 2, Problems and Solutions

PROBLEMS ON ABSTRACT ALGEBRA

Exercises 1 Sets and functions

TEACHER CERTIFICATION STUDY GUIDE

2 Geometric interpretation of complex numbers

n=1 a n is the sequence (s n ) n 1 n=1 a n converges to s. We write a n = s, n=1 n=1 a n

SIGNALS AND SYSTEMS I Computer Assignment 1

A brief introduction to linear algebra

Math 778S Spectral Graph Theory Handout #3: Eigenvalues of Adjacency Matrix

6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer.

) is a square matrix with the property that for any m n matrix A, the product AI equals A. The identity matrix has a ii

1 1 2 = show that: over variables x and y. [2 marks] Write down necessary conditions involving first and second-order partial derivatives for ( x0, y

Mathematics Review for MS Finance Students Lecture Notes

NAME: ALGEBRA 350 BLOCK 7. Simplifying Radicals Packet PART 1: ROOTS

Zeros of Polynomials

Applications in Linear Algebra and Uses of Technology

PROPERTIES OF AN EULER SQUARE

Appendix F: Complex Numbers

Physics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001.

A TYPE OF PRIMITIVE ALGEBRA*

Chapter 6: Determinants and the Inverse Matrix 1

Math 299 Supplement: Real Analysis Nov 2013

LESSON 2: SIMPLIFYING RADICALS

A widely used display of protein shapes is based on the coordinates of the alpha carbons - - C α

Linearly Independent Sets, Bases. Review. Remarks. A set of vectors,,, in a vector space is said to be linearly independent if the vector equation

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

CALCULATION OF FIBONACCI VECTORS

PRELIM PROBLEM SOLUTIONS

Four-dimensional Vector Matrix Determinant and Inverse

Summary: Congruences. j=1. 1 Here we use the Mathematica syntax for the function. In Maple worksheets, the function

A NOTE ON PASCAL S MATRIX. Gi-Sang Cheon, Jin-Soo Kim and Haeng-Won Yoon

In number theory we will generally be working with integers, though occasionally fractions and irrationals will come into play.

Representing transformations by matrices

Review Problems 1. ICME and MS&E Refresher Course September 19, 2011 B = C = AB = A = A 2 = A 3... C 2 = C 3 = =

CHAPTER 5. Theory and Solution Using Matrix Techniques

Chapter 0. Review of set theory. 0.1 Sets

Enumerative & Asymptotic Combinatorics

Course : Algebraic Combinatorics

Thoughts on Interaction

5. Matrix exponentials and Von Neumann s theorem The matrix exponential. For an n n matrix X we define

INTEGRATION BY PARTS (TABLE METHOD)

The Jordan Normal Form: A General Approach to Solving Homogeneous Linear Systems. Mike Raugh. March 20, 2005

Orthogonal transformations

Algebra of Least Squares

Matrix Algebra 2.3 CHARACTERIZATIONS OF INVERTIBLE MATRICES Pearson Education, Inc.

On Nonsingularity of Saddle Point Matrices. with Vectors of Ones

Chapter 7 COMBINATIONS AND PERMUTATIONS. where we have the specific formula for the binomial coefficients:

Introduction to Computational Biology Homework 2 Solution

Math 155 (Lecture 3)

Iterative Techniques for Solving Ax b -(3.8). Assume that the system has a unique solution. Let x be the solution. Then x A 1 b.

Fortgeschrittene Datenstrukturen Vorlesung 11

Transcription:

(IC) Matrix algebra Before formalizig Gauss-Jorda i terms of a fixed procedure for row-reducig A, we briefly review some properties of matrix multiplicatio Let m{ [A ij ], { [B jk ] p, p{ [C kl ] q be matrices, with etries i (say) R, or more geerally ay field (cf IIA) Recall that the traspose t A is the m matrix with etries ( t A) ij = A ji I write the superscript t o the left so that later we ca talk about the traspose iverse t A without paretheses The m m idetity matrix with etries if i = j ad 0 otherwise will be deoted by I m (or just I); ad we will write E m ij (or just E ij) for the matrix with (i, j) th etry ad all other etries zero Multiplicatio: At ay rate, we defie the matrix product AB to be the m p matrix with etries (AB) ik := A ij B jk j= Associativity of this product follows from associativity of the groud field: ((AB)C) il := k = j,k (AB) ik C kl = k ( j A ij (B jk C kl ) = = (A(BC)) il A ij B jk )C kl = (A ij B jk )C kl j,k

2 (IC) MATRIX ALGEBRA Commutativity fails: BA is ot eve defied uless p = m, i which case the closest oe has is BA = t ( t A t B) A example where A ad B are actually symmetric: [ ] [ ] [ ] [ ] [ ] [ ] 0 0 0 0 0 0 0 = = = 0 0 0 0 0 0 0 0 0 For a physicist, ocommutativity is essetial, sice it s the etire poit of the Heiseberg ucertaity priciple that the positio ad mometum operators do t commute! Or as the Mad Hatter says, seeig what you eat ad eatig what you see are ot at all the same thig Iverses: if A is m for m <, it caot have a left iverse (L such that LA = I ) but may have may right iverses (R such that AR = I m ) A example, where a, b ca be ay real umbers: A {[ }} ]{ t 0 0 0 at bt a b 0 = [ 0 0 If m > the the situatio is just reversed For square matrices (m = ) we will prove i ID that { of a left iverse} { of a right iverse} But if both exist for a matrix A, they must be the same: BA = I, AC = I = B = BI = B(AC) = (BA)C = IC = C I this situatio we say A is ivertible, deotig the (left ad right) iverse matrix by A Products ad iverses of ivertible matrices are ivertible; eg for products AB, B A furishes a 2 -sided iverse EXAMPLE If αδ βγ = 0 (for α, β, γ, δ i your favorite field), ] we have ( α γ β δ ) = ( αδ βγ δ γ β α ) though puttig these words i the Mad Hatter s mouth may have bee a polemic o Lewis Carroll s part agaist the quaterios

(IC) MATRIX ALGEBRA 3 Vectors ad matrix multiplicatio: For x R, here are some characterizatios of the matrix-vector product A x i terms of rows ad colums of A : = c A x = c c c x x x = r x r m x = x c + + x c = x i c i i= Writig ê i for the coordiate vectors of R, we see that Aê i = c i ad so A = Aê Aê (With this uderstood, you should ow be able to easily covice yourself that the colums of a matrix product AB are liear combiatios of the colums of A!) There are two differet ways to multiply vectors as matrices: 3 [ 2 ] = 4 = dot (iterior) product, 2 6 2 2 [ 3 ] = 3 = exterior product 3 I particular, if x ad y are two colum vectors, the the dot product x y i terms of matrix multiplicatio is t X Y (where X ad Y are the correspodig m matrices)

4 (IC) MATRIX ALGEBRA Elemetary matrices: These are m m (square) matrices of oe of the followig three types: Sij m := I m Eii m Em jj + Em ij + Em ji = 0 0, Si m ( a ) := I m + ( a )E ii = a, ad R m ji ( b) := I m be m ji = b I ll drop the superscript m i the sequel sometimes The elemetary row operatios of IB may be iterpreted as left-multiplyig the augmeted matrix (represetig our liear system) by oe of these elemetary matrices: the first exchages the i th ad j th rows of the matrix it operates o; the secod divides the i th row by a; ad the third subtracts b (i th row) from the j th row All three types of matrices are clearly ivertible, with S ij = S ij, S i ( a ) = S i (a), ad R ji ( b) = R ji (b) Exercises () Fid two differet 2 2 matrices A such that A 2 = 0 but A = 0 (2) By carryig out Gauss-Jorda ad keepig track of your steps, fid elemetary matrices E,, E k such that E k E 2 E A = I,

where A := EXERCISES 5 2 0 3 0 (3) Let A be a upper triagular m m matrix (That is, A ij = 0 for i > j) Show that A is ivertible if ad oly if all the diagoal etries A ii are ozero (4) Cosider the set H M 2 (C) (of 2 2 matrices with complex etries) of the form ( ) ( α β a x = = 0 + a β ᾱ a 2 + a 3 a2 + a 3 a0 a ), a i R Show that H is closed uder additio ad multiplicatio, ad that every ozero elemet is ivertible show that multiplicatio is ot commutative Give a example to