Module 6: LINEAR TRANSFORMATIONS

Similar documents
Chapter 3 MATRIX. In this chapter: 3.1 MATRIX NOTATION AND TERMINOLOGY

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

ECON 331 Lecture Notes: Ch 4 and Ch 5

a a a a a a a a a a a a a a a a a a a a a a a a In this section, we introduce a general formula for computing determinants.

Introduction To Matrices MCV 4UI Assignment #1

MATRICES AND VECTORS SPACE

Chapter 2. Determinants

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

HW3, Math 307. CSUF. Spring 2007.

Math Lecture 23

Quadratic Forms. Quadratic Forms

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

INTRODUCTION TO LINEAR ALGEBRA

Geometric Sequences. Geometric Sequence a sequence whose consecutive terms have a common ratio.

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes

Determinants Chapter 3

CHAPTER 2d. MATRICES

Chapter 14. Matrix Representations of Linear Transformations

The graphs of Rational Functions

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24

1 Linear Least Squares

Multivariate problems and matrix algebra

13: Diffusion in 2 Energy Groups

BIFURCATIONS IN ONE-DIMENSIONAL DISCRETE SYSTEMS

Matrices and Determinants

Numerical Linear Algebra Assignment 008

Improper Integrals, and Differential Equations

Elements of Matrix Algebra

Here we study square linear systems and properties of their coefficient matrices as they relate to the solution set of the linear system.

Review of basic calculus

Matrix Eigenvalues and Eigenvectors September 13, 2017

Review of Calculus, cont d

Introduction to Determinants. Remarks. Remarks. The determinant applies in the case of square matrices

1.2. Linear Variable Coefficient Equations. y + b "! = a y + b " Remark: The case b = 0 and a non-constant can be solved with the same idea as above.

AQA Further Pure 1. Complex Numbers. Section 1: Introduction to Complex Numbers. The number system

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac

Chapter 3. Vector Spaces

The Periodically Forced Harmonic Oscillator

Kinematics equations, some numbers

The Regulated and Riemann Integrals

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for.

Algebra Of Matrices & Determinants

NOTE ON TRACES OF MATRIX PRODUCTS INVOLVING INVERSES OF POSITIVE DEFINITE ONES

2. VECTORS AND MATRICES IN 3 DIMENSIONS

The area under the graph of f and above the x-axis between a and b is denoted by. f(x) dx. π O

The Algebra (al-jabr) of Matrices

A Matrix Algebra Primer

W. We shall do so one by one, starting with I 1, and we shall do it greedily, trying

Chapter 3 Polynomials

Chapter 5 Determinants

Ordinary differential equations

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!!

ODE: Existence and Uniqueness of a Solution

308K. 1 Section 3.2. Zelaya Eufemia. 1. Example 1: Multiplication of Matrices: X Y Z R S R S X Y Z. By associativity we have to choices:

Year 11 Matrices. A row of seats goes across an auditorium So Rows are horizontal. The columns of the Parthenon stand upright and Columns are vertical

Recitation 3: Applications of the Derivative. 1 Higher-Order Derivatives and their Applications

A REVIEW OF CALCULUS CONCEPTS FOR JDEP 384H. Thomas Shores Department of Mathematics University of Nebraska Spring 2007

Integrals - Motivation

SUMMER KNOWHOW STUDY AND LEARNING CENTRE

Chapter 3 Solving Nonlinear Equations

Math 270A: Numerical Linear Algebra

Matrix Solution to Linear Equations and Markov Chains

Exponentials - Grade 10 [CAPS] *

Matrices. Elementary Matrix Theory. Definition of a Matrix. Matrix Elements:

Exam 2, Mathematics 4701, Section ETY6 6:05 pm 7:40 pm, March 31, 2016, IH-1105 Instructor: Attila Máté 1

Absolute values of real numbers. Rational Numbers vs Real Numbers. 1. Definition. Absolute value α of a real

Bases for Vector Spaces

Recitation 3: More Applications of the Derivative

Continuous Random Variables

dx dt dy = G(t, x, y), dt where the functions are defined on I Ω, and are locally Lipschitz w.r.t. variable (x, y) Ω.

a < a+ x < a+2 x < < a+n x = b, n A i n f(x i ) x. i=1 i=1

Math 360: A primitive integral and elementary functions

Things to Memorize: A Partial List. January 27, 2017

DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ

CHAPTER 1 PROGRAM OF MATRICES

Engineering Analysis ENG 3420 Fall Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00

MATH , Calculus 2, Fall 2018

Lesson 1: Quadratic Equations

OXFORD H i g h e r E d u c a t i o n Oxford University Press, All rights reserved.

1.1. Linear Constant Coefficient Equations. Remark: A differential equation is an equation

Theoretical foundations of Gaussian quadrature

Review of Gaussian Quadrature method

Math 8 Winter 2015 Applications of Integration

Coalgebra, Lecture 15: Equations for Deterministic Automata

Chapter 5 : Continuous Random Variables

We will see what is meant by standard form very shortly

TABLE OF CONTENTS 3 CHAPTER 1

Math 113 Exam 2 Practice

Advanced Calculus: MATH 410 Notes on Integrals and Integrability Professor David Levermore 17 October 2004

Math 4310 Solutions to homework 1 Due 9/1/16

DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ

Exploring parametric representation with the TI-84 Plus CE graphing calculator

dt. However, we might also be curious about dy

Math 61CM - Solutions to homework 9

Lecture Solution of a System of Linear Equation

Math 31S. Rumbos Fall Solutions to Assignment #16

Matrix & Vector Basic Linear Algebra & Calculus

REVIEW Chapter 1 The Real Number System

In Section 5.3 we considered initial value problems for the linear second order equation. y.a/ C ˇy 0.a/ D k 1 (13.1.4)

Transcription:

Module 6: LINEAR TRANSFORMATIONS. Trnsformtions nd mtrices Trnsformtions re generliztions of functions. A vector x in some set S n is mpped into m nother vector y T( x). A trnsformtion is liner if, for ech component yi of the vector y is liner function of the vector x, tht is, yi i x. It is useful to use mtrix rrys to exmine liner trnsformtions. Consider mtrix A with m rows nd p columns nd second mtrix B with p rows nd n columns. We define the new mtrix C to be the product of A nd B if the ijth element of C is the product of two vectors, the ith row of A nd the jth column of B. An exmple is shown below. Note tht the product of two mtrices A nd B is only defined if the number of columns of A is equl to the number of rows of A. 2 2 4 2 C AB 3, 3 3 3 2 2 C AB 3 Then liner trnsformtion is the product of the mnmtrix A nd the column vector x. y A x Exercise.: Rules of mtrix lgebr () Follow the definition of mtrix multipliction nd estblish the following rules of mtrix lgebr for the cse. (i) A( x z) Ax A z (where nd re prmeters.) (ii) A( B +C) (iii) A( Bx) ( AB ) x AB + AC. (b) The specil mtrix with ones down the digonl nd zeroes elsewhere is the identity mtrix nd written s I. Confirm tht the identity mtrix mps vector onto itself, tht is Ix=x.

Inverse of mtrix Consider the mtrix A 2 2. The ij-th sub-mtrix of A is the mtrix creted by deleting the i-th row nd j-th columns. In the 2 2 cse ech sub-mtrix is just number. They re shown below s second mtrix. m m M 2 2 m2 m 2. Flipping mtrix on its side is clled trnsposing mtrix. The trnsposed mtrix is written s m M m m 2 m 2 Next define the cofctor of the number is odd nd consider the mtrix m to be ( ) i c j m so tht the sign chnges if i j ij ij ij 2 3 c c2 ( ) m ( ) m 2 m m2 2 C 3 4 c2 c ( ) m2 ( ) m m 2 m 2. Define the determinnt of A A 2 2 As is esily checked, A AC = CA A A I. A As long s the determinnt of A is not zero we cn therefore define the inverse mtrix B C A. Then we hve proved tht if A AB=BA=I. We use the nottion A to denote the inverse of A. 2

Then AA A A I. Solution of liner eqution system Consider the liner system of equtions Ax=b where A. Multiply both side by the inverse mtrix A A b A ( Ax) ( A A) x I x x. Exercise.2: Solution of liner eqution system 8 A 7 5 () Solve for the inverse mtrix. (b) Solve the liner eqution system Ax=b if 4 b 3 (c) For generl vector b solve for x(b), the solution to Ax=b. Exercise.3: Liner dependence () Show tht A 2 x x x2 2. (b) If A explin why AC=. (c) Hence show tht 2 c c2 2 Thus the two column vectors of A re linerly relted. (d) Show lso tht the two rows of A re linerly relted. 3

2. Dynmics of liner systems The stte of n economic system is chrcterized by the column vector x t x () t x2() t ( ), t,2.... The initil stte is x () x2() The economy evolves ccording to the following liner system of equtions. x( t ) A x( t), tht is, x ( t ) 2 x ( t) x ( t ) x ( t) 2 2 2 Then x(2) A x(), 2 x(3) Ax(2) A( Ax()) A x(),. x t t ( ) x() A. It is helpful to use spredsheet to compute the sequence x(), x(2),..x(t) for some finite number of periods. Downlod the spredsheet nd you will find tht the mtrix is the one in Exercise 2.: Explosive dynmics? Sheet A ndsheetb depict the evolution of x(t) for T=5 nd T= nd different strting stte x(). () Use SheetC to see how the stte evolves for different initil sttes. (b) Show tht the evolution depicted in one of the first two sheets is unusul. Suggestion: The strting stte for SheetA is on the line. Try strting sttes (i) on this line nd (ii) ner this line. (c) Confirm your conclusions using Sheet2B one which llows choice of how mny periods (up to.) (d) Wht cn you conclude bout the rtio x2( t) / x( t) s t increses for lmost ll initil sttes? 4

Exercise 2.2: x( t ).25 x( t) x ( t ).25 x ( t) 2 2 () Does the stte vry pproximtely s depicted for ll initil x()? (b) Vry the prmeters of the mtrix to see some of the possible outcomes. In prticulr, by incresing or decresing the prmeters long the leding digonl ( nd ) try to chrcterize prmeters tht led the system to dmped oscilltions nd prmeters tht led the system to oscillte explosively. 5

3. Chrcteriztion of the dynmics of liner system (to be discussed in Mth Cmp) To understnd the dynmics we first sk if there is strting point x() such tht the stte simply grows t constnt rte, tht is x( t ) x( t). We cn rewrite this in mtrix form s follows. x t x ( t ) x ( t) ( ) x( t) x2( t ) x2( t) I Since x( t ) A x( t), it follows tht Ax( t) I x( t), tht is B xt ( ) where B A I. If B is invertible xt ( ) B. Thus if we re to find some non zero x () it must be the cse tht B cnnot be inverted. As we hve seen this is the cse if nd only of the determinnt of B is equl to zero. Thus b b b 2 2 2 2 b2 b b2 b ( )( ). Exercise 3. () Consider the mtrix 4 2 A 3. Solve for the two chrcteristic roots (, ). (b) For the first of these chrcteristic roots show tht the stte grows t constnt rte if the x () strting stte is x () x (). Solve lso for the strting stte vector grows t the constnt rte. x x () () x2 () if the stte (c) For ny strting point x () explin why there is unique such tht x() x () ( ) x () (d) Hence show tht x(2) x () ( ) x (). 6

HINT: Remember tht Ax () x () nd Ax () x (). t t (e) Repet the rgument to show tht x( t) x () ( ) x (). (f) Hence explin why, for lrge t, the system grows t pproximtely the rte of the chrcteristic root with the lrgest bsolute vlue. (g) Suppose tht 2 A Use your spred-sheet to depict the evolution of the stte. Solve for the chrcteristic roots if ny. 7

Answers for section Exercise.2: A =- () 2 2 8 A 7 5, 5 7 M 8, 5 8 5 8 M 7 C 7, 5 8 5 8 A 7 7. A It is esy to check the result by multiplying 8 A 7 5 nd 5 8 7 (b) Ax=b. Therefore x x x b 5 8 b - - I A A A 7 b 2 Exercise.3: () 2 x x 2 x2 A x x x x 2 2 2 2 2 x 2 x2 x 2 x2 x x2 x 2 2x x 2 2x x A 2 (b) As shown bove, A AC = CA A A I. A Therefore if A, (c) It follows tht c c 2 2 AC 2 c2 c. 2 c 2 c c2 2 c 2 2 8

Exercise 2. The chrt below shows xt () when T =24 nd the strting stte is very close to the line x x. Note tht for the first 5 periods the stte pproches the origin. But x(t) eventully 5 2 2 strts veering wy. If you choose higher T you will see tht x(t) eventully gets close to the line x2 x. This is 5 true for ll strting sttes not on the line x x. Thus the rtio x2( t) / x( t ) pproches. 2 2 5 Note finlly tht if the strting point is bove the line x x then x () t is lrge nd positive 2 2 for lrge T. If the strting point is below the line then x () t is lrge nd negtive for lrge T. 9

Exercise 2.2 The chrt below shows cse in which the oscilltions re lmost stble. However in this cse the oscilltions re slightly dmped. For higher vlues on the leding digonl the oscilltions become explosive. For lower vlues the cycles become more dmped.