Key Point. The nth order linear homogeneous equation with constant coefficients

Size: px
Start display at page:

Download "Key Point. The nth order linear homogeneous equation with constant coefficients"

Transcription

1 General Solutions of Higher-Order Linear Equations In section 3.1, we saw the following fact: Key Point. The nth order linear homogeneous equation with constant coefficients a n y (n) a 2 y + a 1 y + a 0 y = 0 has n essentially different solutions y 1, y 2,...,y n, and Y = c 1 y 1 + c 2 y c n y n, where each c i is an arbitrary constant, is the most general solution to the equation. In section 3.3, we learned how to find these n essentially different solutions to an nth order LHCC equation, and were thus able to build the general solution. In this section, we will discuss the phrase essentially different in detail; in addition, we will learn how solutions to LHCC equations can be used to help solve the associated linear nonhomogeneous equations with constant coefficients. We will need to recall the special forms in which higher-order equations can appear; an equation is linear if it can be written in the form A n (x)y (n) A 2 (x)y + A 1 (x)y + A 0 (x)y = F (x); a linear equation is homogeneous if the only term not containing y or one of its derivatives is 0, i.e. the equation has the form A n (x)y (n) A 2 (x)y + A 1 (x)y + A 0 (x)y = 0; a linear equation has constant coefficients if it can be written in the form a n y (n) a 2 y + a 1 y + a 0 y = F (x), where each a 1 is a constant; and a linear homogeneous equation with constant coefficients (LHCC equation) has form a n y (n) a 2 y + a 1 y + a 0 y = 0. One final note: many of the theorems in this section apply to all linear equations, or to all linear homogeneous equations, and not just to LHCC equations. Particular Solutions We have seen that we can use known solutions to an equation to build new ones. In section 3.1, we saw that v 1 = sin x and v 2 = cos x are solutions to y + y = 0; but so are q = 3 sin x cos x, r = 5 sin x 3 + π cos x, and s = cos x. The idea of using a few solution functions (like v 1 and v 2 ) to build many more (like q, r, and s) is an extremely important one, so we will give this idea a name: 1

2 Definition. A linear combination of functions y 1, y 2,..., y n, is any sum of constant multiples of the y i, and has the form c 1 y 1 + c 2 y c n y n, where each c i is a constant. In the language of this new definition, each of q, r, and s is a linear combination of v 1 and v 2. It is easy to see that q = 3v 1 v 2, r = 5v πv 2, and s = v 2. It turns out that any linear combinations of solutions to a linear homogeneous equation (with or without constant coefficients) is also a solution, as indicated by the following theorem: Theorem 1. Let y 1,..., y n be n (particular) solutions of an nth order linear homogeneous equation on an interval I. Then the linear combination y = c 1 y c n y n is also a (particular) solution of the equation on the interval I for any constants c 1,..., c n. In section 3.1, we saw the following point: Key Point. We must have n initial conditions in order to find a particular solution to an nth order LHCC equation. We will state this theorem precisely in a moment; but before we do, we should think back to the analogous idea from our study of first-order equations. When we worked with first-order equations, we were concerned that an initial value problem (an equation along with one initial condition) might have no solution, or might have more than one solution; but in chapter 1, we saw that well-behaved equations will have precisely one solution corresponding to the initial condition. We may have some of the same concerns about finding particular solutions to higher-order equations; given a linear nth order equation and n initial conditions, can we be certain that there will be a solution? Can we be sure that there is no more than one solution? The theorem below answers that question; as with first-order equations, if our linear nth order equation is reasonably well-behaved, we can be certain that the initial value problem will have precisely one solution: Theorem 2. If p 1 (x), p 2 (x),..., p n (x) and f(x) are continuous on an open interval I, and if a is in the interval I, then the nth order linear equation y (n) + p 1 (x)y (n 1) p n 1 (x)y + p n (x)y = f(x) has precisely one solution on the entire interval I satisfying the conditions y(a) = b 0, y (a) = b 1,..., y (n 1) (a) = b n 1. 2

3 Applying this theorem to LHCC equations gives us a particularly nice result; since an LHCC equation has form a n y (n) a 2 y + a 1 y + a 0 y = 0, each of the functions p i (x) from Theorem 2 are just constant functions. Of course, constant functions are continuous on the entire real line, so when we study LHCC equations, we can be certain that their solutions are unique for all real numbers. General Solutions Notice that Theorem 1 above specifically deals with particular solutions, not with finding a general solution to the given linear homogeneous equation. In terms of general solutions, recall that we have seen the following point several times: Key Point. The nth order linear homogeneous equation with constant coefficients a n y (n) a 2 y + a 1 y + a 0 y = 0 has n essentially different solutions y 1, y 2,...,y n, and Y = c 1 y 1 + c 2 y c n y n, where each c i is an arbitrary constant, is the most general solution to the equation. We are now ready to be more precise about what we mean by the phrase essentially different. We ll start with a couple of examples; in the first, we will look at three functions that I claim are not essentially different. In the second example, the three functions presented are essentially different. Example. Let f 1 = x 1, f 2 = 3x, and f 3 = 2. In a sense, these functions are not essentially different because it is possible to build one of them from the other two by using linear combinations. For example, we can view f 1 as a linear combination of f 2 and f 3 by writing In a sense, f 1 depends on f 2 and f f f 3 = 1 3 (3x) 1 2 (2) = x 1 = f 1. Example. On the other hand, g 1 = 1, g 2 = x, and g 3 = x 2 are essentially different: it is impossible to build any one of them from the other two using linear combinations. For example, the linear combination c 1 g 1 + c 2 g 2 = c 1 + c 2 x can never produce g 3 = x 2, no matter how well we pick the constants c 1 and c 2. In a sense, these three functions are independent of each other. 3

4 In the first example above, we wrote alternatively, 1 3 f f 3 = f 1 ; f f f 3 = 0. In the second example, we tried to write or c 1 g 1 + c 2 g 2 = g 3, c 1 g 1 + c 2 g 2 g3 = 0, but saw that this was impossible. We give a name to each type of behavior in the definition below: Definition. The functions f 1,..., f n are linearly dependent if there are constants c 1,..., c n (at least one of which is non-zero) so that c 1 f c n f n = 0. If there is no way to find constants c 1,..., c n (with at least one c i 0) so that c 1 f c n f n = 0, then the functions f 1,..., f n are linearly independent. Using this language, we would describe the three functions f 1 = x 1, f 2 = 3x, and f 3 = 2 as linearly dependent, while we would say that the functions are linearly independent. g 1 = 1, g 2 = x, and g 3 = x 2 Saying that a collection of functions is linearly independent is a precise way to say that they are essentially different. With this in mind, we are now ready to discuss the general solutions of linear homogeneous equations. Notice that the theorem below applies to any linear homogeneous equation, not just to LHCC equations. Theorem 4. If y 1, y 2,..., and y n are linearly independent solutions to a linear homogeneous nth order equation, then the general solution to the equation is Y = c 1 y 1 + c 2 y c n y n, where each c i is an arbitrary constant, and any solution to the equation is of this form. 4

5 The most important point to note here is this: any particular solution to a linear homogeneous nth order equation is just a linear combination of the n linearly independent solutions y 1,...,y n. We can apply Theorem 4 specifically to LHCC equations; from Section 3.3, we know how to find n solutions to any nth order LHCC equation, and we will soon be able to show that these n solutions are linearly independent. Thus the technique we learned in Section 3.3 does actually produce the general solution. Because of Theorem 4, it will be extremely important to be able to tell if a system of n equations is actually linearly independent. For example, suppose that we know that a particular third order linear homogeneous equation has solutions y 1 = 2x 3, y 2 = 2x 2 + 1, y 3 = x 2 + 1, y 4 = 2x 2 x, and y 5 = 3x 2 + x, and we would like to be able to write a general solution. Since the original equation is third order, we simply need to find three linearly independent solutions so we hope that three of the solutions above are linearly independent. Unfortunately, it is quite difficult to do this by inspection; for example, if we think that y 1, y 2, and y 3 are linearly independent, then we have to show that there are no constants c 1, c 2, and c 3 (at least one of which is nonzero) so that c 1 y 1 + c 2 y 2 + c 3 y 3 = 0. Fortunately, there is a tool that we can use to check a set of functions for independence or dependence. This tool is called the Wronskian, and is defined below: Definition. Assume that the functions f 1,...,f n are n times differentiable (i.e. f i, f i (n),...,f i all exist for each of the functions). The Wronskian of the functions f 1,...,f n is the determinant W of the n n matrix f 1 f 2... f n f 1 f 2... f n.... f (n) 1 f (n) 2... f n (n) The utility of the Wronskian in determining if n functions are independent or dependent is apparent due to the following theorem: Theorem 3. Suppose that y 1,..., y n are n solutions of the linear homogeneous nth order equation y (n) + p 1 (x)y (n 1) p n 1 (x)y + p n (x)y = 0 on an open interval I, and let W be the Wronskian of the functions. If each p i is continuous, then: (a) If y 1,...,y n are linearly dependent, then W = 0 on each point of I. (b) If y 1,...,y n are linearly independent, then W 0 on each point of I. The theorem is extremely helpful: since a collection of functions must be either dependent or independent, then their Wronskian will either be identically 0 (if they are dependent), or never 0 (if they are independent). Putting Theorems 3 and 4 together gives us a method for finding the general solution to an nth order linear homogeneous equation: 5

6 Key Point. To find the general solution of an nth order linear homogeneous equation, find n functions y 1,...,y n, whose Wronskian is nonzero. Since these n functions are then linearly independent, Theorem 4 says that y = c 1 y 1 + c 2 y c n y n is the general solution to the equation, and any solution is of this form. We see from the remark above that finding the general solution to an equation will come down to evaluating the Wronskian, which is just a matrix determinant. We should recall how to make the calculation for 2 2 and 3 3 determinants: The determinant of a 2 2 matrix ( ) a b, c d denoted by is a b c d The determinant of a 3 3 matrix denoted by is which can be expanded as So a b c d e f g h j a e h a c b d, = ad bc. a b c d e f, g h j a b c d e f g h j f j b d g, f j + c d g e h a(ej fh) b(dj fg) + c(dh eg). = a(ej fh) b(dj fg) + c(dh eg). There is an alternate algorithm for finding determinants of 3 3 matrices that makes the computation a bit quicker. As an example, consider the matrix Start by putting the first two columns at the end of the matrix, so that we know have a 3 5 array: 6

7 The lines drawn below in the array indicate lines along which we must multiply; blue lines indicate that we leave the sign of the product the same, while red lines indicate that we must change the sign of the product: Finally, we add up the products of the lines; in this case, the determinant of the matrix is = = Example. A particular third order linear homogeneous equation has solutions y 1 = 2x 3, y 2 = 2x 2 + 1, y 3 = x 2 + 1, y 4 = 2x 2 x, and y 5 = 3x 2 + x. Find a general solution to the equation. We need to find three functions from the list above whose Wronskian is nonzero; we may have to try several combinations to find such a triplet. Let s start with y 1, y 2, and y 5. Their Wronskian is the determinant of the matrix y 1 y 2 y 5 2x 3 2x x 2 + x y 1 y 2 y 5 = 2 4x 6x + 1. y 1 y 2 y Rewriting the matrix as 2x 3 2x x 2 + x 2x 3 2x x 6x x

8 we can now calculate the determinant by multiplying along diagonals: the determinant is W (y 1, y 2, y 5 ) = (2x 3)(4x)(6) + (2x 2 + 1)(6x + 1)(0) + (3x 2 + x)(2)(4) (2x 2 + 1)(2)(6) (2x 3)(6x + 1)(4) (3x 2 + x)(4x)(0) = 48x 2 72x + 24x 2 + 8x 24x 2 12 (8x 12)(6x + 1) = 48x 2 72x + 24x 2 + 8x 24x 2 12 (48x 2 72x + 8x 12) = 48x 2 72x + 24x 2 + 8x 24x x x 8x + 12 = 24x 2 24x x 2 48x 2 72x + 8x + 72x 8x = 0. Since W (y 1, y 2, y 5 ) = 0, the three functions are linearly dependent; in other words, while the linear combination c 1 y 1 + c 2 y 2 + c 3 y 5 can be used to describe many solutions of the equation, it will miss some other solutions entirely. We need to try again: let s use y 1, y 3, and y 4. Their Wronskian is the determinant of the matrix y 1 y 3 y 4 2x 3 x x 2 x y 1 y 3 y 4 = 2 2x 4x 1. y 1 y 3 y To calculate the determinant, we use 2x 3 x x 2 x 2x 3 x x 4x 1 2 2x Multiplying along diagonals, we get W (y 1, y 3, y 4 ) = (2x 3)(2x)(4) + (x 2 + 1)(4x 1)(0) + (2x 2 x)(2)(2) (x 2 + 1)(2)(4) (2x 3)(4x 1)(2) (2x 2 x)(2x)(0) = 16x 2 24x + 8x 2 4x 8x 2 8 (2x 3)(8x 2) = 16x 2 24x + 8x 2 4x 8x x x + 4x 6 = 16x 2 + 8x 2 8x 2 16x 2 24x + 24x 4x + 4x 8 6 = 14. Since W (y 1, y 3, y 4 ) 0, the three functions are linearly independent, and the most general solution to the original equation is y = c 1 y 1 + c 2 y 3 + c 3 y 4 ; any solution to the original equation is of this form. 8

9 Nonhomogeneous Equations Recall that a linear nonhomogeneous equation has associated homogeneous equation y (n) A 2 (x)y + A 1 (x)y + A 0 (x)y = F (x) y (n) A 2 (x)y + A 1 (x)y + A 0 (x)y = 0. Our original interest in homogeneous equations stemmed from the fact that we could use them to help find general solutions to nonhomogeneous equations. The following theorem tells us how this interaction plays out: Theorem 5. Suppose that y p is a solution of the nth order linear nonhomogeneous equation y (n) + p 1 (x)y (n 1) p n 1 (x)y + p n (x)y = f(x) on an open interval I, and that each p i and f(x) are continuous on I. If y c is a solution of the associated homogeneous equation, then y = y p + y c is also a solution of the nonhomogeneous equation on I. If y 1,...,y n are linearly independent solutions to the associated nth order equation, then the general solution of the nonhomogeneous equation is Y = c 1 y c n y n + y p, and every solution to the nonhomogeneous equation is of this form. We will call a solution y p of the nonhomogeneous equation a particular solution; any solution y c of the associated homogeneous equation is called a complementary solution. The theorem tells us that we can find the general solution to the nonhomogeneous equation by finding any solution to the original equation, then adding it to the general solution of the associated homogeneous equation. Example. Find a general solution to y y = e 2x. We should begin by trying to find a particular solution: since the difference of y and the original function y is e 2x, it seems reasonable to guess that y should be something like e 2x ; perhaps a constant multiple of this would work. If y = ce 2x, then y = 2ce 2x and y = 4ce 2x. So y y = 4ce 2x ce 2x = 3ce 2x. 9

10 Since we want y y = e 2x, we should choose c = 1 3. It is clear that y p = 1 3 e2x is the particular solution we re looking for. To find a general solution to the equation, we need to find a general solution to the associated homogeneous equation y y = 0. The characteristic equation is r 2 r = 0, and since r 2 r factors as (r 0)(r 1), the roots are r 1 = 0 and r 2 = 1. The general solution to the homogeneous equation is thus and the general solution to y y = e 2x is y c = c 1 e 0x + c 2 e x = c 1 + c 2 e x, Y = c 1 + c 2 e x e2x. Theorem 5 may seem a bit unbelievable why should a solution to a homogeneous equation help us build the general solution to a nonhomogeneous equation? It seems that we lose a lot of information when we switch to the homogeneous version of the equation. Let s think about the theorem using our example above. Since y c = c 1 + c 2 e x is a solution to y y = 0, we know that y c y c = 0. In addition, we know that y p = 1 3 e2x is a solution to y y = e 2x, so that Adding these two equalities gives us y p y p = e 2x. which we can rewrite as y c y c + y p y p = e 2x, (y c + y p ) (y c + y p ) = e 2x, proving that y c + y p is indeed the general solution we were looking for. Finding the particular solution y p to the nonhomogeneous equation may seem a bit daunting; we will study this problem more in section

y 2y = 4 x, Name Form Solution method

y 2y = 4 x, Name Form Solution method An Introduction to Higher-Order Differential Equations Up to this point in the class, we have only specifically studied solution techniques for first-order differential equations, i.e. equations whose

More information

Determinants of 2 2 Matrices

Determinants of 2 2 Matrices Determinants In section 4, we discussed inverses of matrices, and in particular asked an important question: How can we tell whether or not a particular square matrix A has an inverse? We will be able

More information

Unit 2, Section 3: Linear Combinations, Spanning, and Linear Independence Linear Combinations, Spanning, and Linear Independence

Unit 2, Section 3: Linear Combinations, Spanning, and Linear Independence Linear Combinations, Spanning, and Linear Independence Linear Combinations Spanning and Linear Independence We have seen that there are two operations defined on a given vector space V :. vector addition of two vectors and. scalar multiplication of a vector

More information

Section 1.8 Matrices as Linear Transformations

Section 1.8 Matrices as Linear Transformations Section.8 Matrices as Linear Transformations Up to this point in the course, we have thought of matrices as stand alone constructions, objects of interest in their own right. We have learned multiple matrix

More information

Homogeneous Linear Systems and Their General Solutions

Homogeneous Linear Systems and Their General Solutions 37 Homogeneous Linear Systems and Their General Solutions We are now going to restrict our attention further to the standard first-order systems of differential equations that are linear, with particular

More information

Math 240 Calculus III

Math 240 Calculus III DE Higher Order Calculus III Summer 2015, Session II Tuesday, July 28, 2015 Agenda DE 1. of order n An example 2. constant-coefficient linear Introduction DE We now turn our attention to solving linear

More information

3 Algebraic Methods. we can differentiate both sides implicitly to obtain a differential equation involving x and y:

3 Algebraic Methods. we can differentiate both sides implicitly to obtain a differential equation involving x and y: 3 Algebraic Methods b The first appearance of the equation E Mc 2 in Einstein s handwritten notes. So far, the only general class of differential equations that we know how to solve are directly integrable

More information

Work sheet / Things to know. Chapter 3

Work sheet / Things to know. Chapter 3 MATH 251 Work sheet / Things to know 1. Second order linear differential equation Standard form: Chapter 3 What makes it homogeneous? We will, for the most part, work with equations with constant coefficients

More information

Slope Fields and Differential Equations. Copyright Cengage Learning. All rights reserved.

Slope Fields and Differential Equations. Copyright Cengage Learning. All rights reserved. Slope Fields and Differential Equations Copyright Cengage Learning. All rights reserved. Objectives Review verifying solutions to differential equations. Review solving differential equations. Review using

More information

Homework #6 Solutions

Homework #6 Solutions Problems Section.1: 6, 4, 40, 46 Section.:, 8, 10, 14, 18, 4, 0 Homework #6 Solutions.1.6. Determine whether the functions f (x) = cos x + sin x and g(x) = cos x sin x are linearly dependent or linearly

More information

8.1 Solutions of homogeneous linear differential equations

8.1 Solutions of homogeneous linear differential equations Math 21 - Spring 2014 Classnotes, Week 8 This week we will talk about solutions of homogeneous linear differential equations. This material doubles as an introduction to linear algebra, which is the subject

More information

5.2 Infinite Series Brian E. Veitch

5.2 Infinite Series Brian E. Veitch 5. Infinite Series Since many quantities show up that cannot be computed exactly, we need some way of representing it (or approximating it). One way is to sum an infinite series. Recall that a n is the

More information

Determinants and Scalar Multiplication

Determinants and Scalar Multiplication Invertibility and Properties of Determinants In a previous section, we saw that the trace function, which calculates the sum of the diagonal entries of a square matrix, interacts nicely with the operations

More information

Chapter 4. Higher-Order Differential Equations

Chapter 4. Higher-Order Differential Equations Chapter 4 Higher-Order Differential Equations i THEOREM 4.1.1 (Existence of a Unique Solution) Let a n (x), a n,, a, a 0 (x) and g(x) be continuous on an interval I and let a n (x) 0 for every x in this

More information

There are two things that are particularly nice about the first basis

There are two things that are particularly nice about the first basis Orthogonality and the Gram-Schmidt Process In Chapter 4, we spent a great deal of time studying the problem of finding a basis for a vector space We know that a basis for a vector space can potentially

More information

PRELIMINARY THEORY LINEAR EQUATIONS

PRELIMINARY THEORY LINEAR EQUATIONS 4.1 PRELIMINARY THEORY LINEAR EQUATIONS 117 4.1 PRELIMINARY THEORY LINEAR EQUATIONS REVIEW MATERIAL Reread the Remarks at the end of Section 1.1 Section 2.3 (especially page 57) INTRODUCTION In Chapter

More information

Math 360 Linear Algebra Fall Class Notes. a a a a a a. a a a

Math 360 Linear Algebra Fall Class Notes. a a a a a a. a a a Math 360 Linear Algebra Fall 2008 9-10-08 Class Notes Matrices As we have already seen, a matrix is a rectangular array of numbers. If a matrix A has m columns and n rows, we say that its dimensions are

More information

Topic 14 Notes Jeremy Orloff

Topic 14 Notes Jeremy Orloff Topic 4 Notes Jeremy Orloff 4 Row reduction and subspaces 4. Goals. Be able to put a matrix into row reduced echelon form (RREF) using elementary row operations.. Know the definitions of null and column

More information

CHAPTER 5. Higher Order Linear ODE'S

CHAPTER 5. Higher Order Linear ODE'S A SERIES OF CLASS NOTES FOR 2005-2006 TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS DE CLASS NOTES 2 A COLLECTION OF HANDOUTS ON SCALAR LINEAR ORDINARY

More information

Differential Equations

Differential Equations This document was written and copyrighted by Paul Dawkins. Use of this document and its online version is governed by the Terms and Conditions of Use located at. The online version of this document is

More information

And, even if it is square, we may not be able to use EROs to get to the identity matrix. Consider

And, even if it is square, we may not be able to use EROs to get to the identity matrix. Consider .2. Echelon Form and Reduced Row Echelon Form In this section, we address what we are trying to achieve by doing EROs. We are trying to turn any linear system into a simpler one. But what does simpler

More information

Math 308 Midterm Answers and Comments July 18, Part A. Short answer questions

Math 308 Midterm Answers and Comments July 18, Part A. Short answer questions Math 308 Midterm Answers and Comments July 18, 2011 Part A. Short answer questions (1) Compute the determinant of the matrix a 3 3 1 1 2. 1 a 3 The determinant is 2a 2 12. Comments: Everyone seemed to

More information

Theorem 3: The solution space to the second order homogeneous linear differential equation y p x y q x y = 0 is 2-dimensional.

Theorem 3: The solution space to the second order homogeneous linear differential equation y p x y q x y = 0 is 2-dimensional. Unlike in the previous example, and unlike what was true for the first order linear differential equation y p x y = q x there is not a clever integrating factor formula that will always work to find the

More information

Determinants and Scalar Multiplication

Determinants and Scalar Multiplication Properties of Determinants In the last section, we saw how determinants interact with the elementary row operations. There are other operations on matrices, though, such as scalar multiplication, matrix

More information

Chapter 3 Higher Order Linear ODEs

Chapter 3 Higher Order Linear ODEs Chapter 3 Higher Order Linear ODEs Advanced Engineering Mathematics Wei-Ta Chu National Chung Cheng University wtchu@cs.ccu.edu.tw 1 2 3.1 Homogeneous Linear ODEs 3 Homogeneous Linear ODEs An ODE is of

More information

D(f/g)(P ) = D(f)(P )g(p ) f(p )D(g)(P ). g 2 (P )

D(f/g)(P ) = D(f)(P )g(p ) f(p )D(g)(P ). g 2 (P ) We first record a very useful: 11. Higher derivatives Theorem 11.1. Let A R n be an open subset. Let f : A R m and g : A R m be two functions and suppose that P A. Let λ A be a scalar. If f and g are differentiable

More information

numerical analysis 1

numerical analysis 1 numerical analysis 1 1.1 Differential equations In this chapter we are going to study differential equations, with particular emphasis on how to solve them with computers. We assume that the reader has

More information

Linear Algebra for Beginners Open Doors to Great Careers. Richard Han

Linear Algebra for Beginners Open Doors to Great Careers. Richard Han Linear Algebra for Beginners Open Doors to Great Careers Richard Han Copyright 2018 Richard Han All rights reserved. CONTENTS PREFACE... 7 1 - INTRODUCTION... 8 2 SOLVING SYSTEMS OF LINEAR EQUATIONS...

More information

The Theory of Second Order Linear Differential Equations 1 Michael C. Sullivan Math Department Southern Illinois University

The Theory of Second Order Linear Differential Equations 1 Michael C. Sullivan Math Department Southern Illinois University The Theory of Second Order Linear Differential Equations 1 Michael C. Sullivan Math Department Southern Illinois University These notes are intended as a supplement to section 3.2 of the textbook Elementary

More information

Linear Algebra Basics

Linear Algebra Basics Linear Algebra Basics For the next chapter, understanding matrices and how to do computations with them will be crucial. So, a good first place to start is perhaps What is a matrix? A matrix A is an array

More information

Midterm 1 NAME: QUESTION 1 / 10 QUESTION 2 / 10 QUESTION 3 / 10 QUESTION 4 / 10 QUESTION 5 / 10 QUESTION 6 / 10 QUESTION 7 / 10 QUESTION 8 / 10

Midterm 1 NAME: QUESTION 1 / 10 QUESTION 2 / 10 QUESTION 3 / 10 QUESTION 4 / 10 QUESTION 5 / 10 QUESTION 6 / 10 QUESTION 7 / 10 QUESTION 8 / 10 Midterm 1 NAME: RULES: You will be given the entire period (1PM-3:10PM) to complete the test. You can use one 3x5 notecard for formulas. There are no calculators nor those fancy cellular phones nor groupwork

More information

Designing Information Devices and Systems I Fall 2018 Lecture Notes Note Introduction to Linear Algebra the EECS Way

Designing Information Devices and Systems I Fall 2018 Lecture Notes Note Introduction to Linear Algebra the EECS Way EECS 16A Designing Information Devices and Systems I Fall 018 Lecture Notes Note 1 1.1 Introduction to Linear Algebra the EECS Way In this note, we will teach the basics of linear algebra and relate it

More information

A = , A 32 = n ( 1) i +j a i j det(a i j). (1) j=1

A = , A 32 = n ( 1) i +j a i j det(a i j). (1) j=1 Lecture Notes: Determinant of a Square Matrix Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong taoyf@cse.cuhk.edu.hk 1 Determinant Definition Let A [a ij ] be an

More information

General Recipe for Constant-Coefficient Equations

General Recipe for Constant-Coefficient Equations General Recipe for Constant-Coefficient Equations We want to look at problems like y (6) + 10y (5) + 39y (4) + 76y + 78y + 36y = (x + 2)e 3x + xe x cos x + 2x + 5e x. This example is actually more complicated

More information

Ordinary Differential Equations Prof. A. K. Nandakumaran Department of Mathematics Indian Institute of Science Bangalore

Ordinary Differential Equations Prof. A. K. Nandakumaran Department of Mathematics Indian Institute of Science Bangalore Ordinary Differential Equations Prof. A. K. Nandakumaran Department of Mathematics Indian Institute of Science Bangalore Module - 3 Lecture - 10 First Order Linear Equations (Refer Slide Time: 00:33) Welcome

More information

2. Duality and tensor products. In class, we saw how to define a natural map V1 V2 (V 1 V 2 ) satisfying

2. Duality and tensor products. In class, we saw how to define a natural map V1 V2 (V 1 V 2 ) satisfying Math 396. Isomorphisms and tensor products In this handout, we work out some examples of isomorphisms involving tensor products of vector spaces. The three basic principles are: (i) to construct maps involving

More information

UNDETERMINED COEFFICIENTS SUPERPOSITION APPROACH *

UNDETERMINED COEFFICIENTS SUPERPOSITION APPROACH * 4.4 UNDETERMINED COEFFICIENTS SUPERPOSITION APPROACH 19 Discussion Problems 59. Two roots of a cubic auxiliary equation with real coeffi cients are m 1 1 and m i. What is the corresponding homogeneous

More information

Eigenvalues and eigenvectors

Eigenvalues and eigenvectors Roberto s Notes on Linear Algebra Chapter 0: Eigenvalues and diagonalization Section Eigenvalues and eigenvectors What you need to know already: Basic properties of linear transformations. Linear systems

More information

DIFFERENTIAL EQUATIONS

DIFFERENTIAL EQUATIONS DIFFERENTIAL EQUATIONS Basic Concepts Paul Dawkins Table of Contents Preface... Basic Concepts... 1 Introduction... 1 Definitions... Direction Fields... 8 Final Thoughts...19 007 Paul Dawkins i http://tutorial.math.lamar.edu/terms.aspx

More information

Practice problems for Exam 3 A =

Practice problems for Exam 3 A = Practice problems for Exam 3. Let A = 2 (a) Determine whether A is diagonalizable. If so, find a matrix S such that S AS is diagonal. If not, explain why not. (b) What are the eigenvalues of A? Is A diagonalizable?

More information

[Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty.]

[Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty.] Math 43 Review Notes [Disclaimer: This is not a complete list of everything you need to know, just some of the topics that gave people difficulty Dot Product If v (v, v, v 3 and w (w, w, w 3, then the

More information

Table of contents. d 2 y dx 2, As the equation is linear, these quantities can only be involved in the following manner:

Table of contents. d 2 y dx 2, As the equation is linear, these quantities can only be involved in the following manner: M ath 0 1 E S 1 W inter 0 1 0 Last Updated: January, 01 0 Solving Second Order Linear ODEs Disclaimer: This lecture note tries to provide an alternative approach to the material in Sections 4. 4. 7 and

More information

Separable First-Order Equations

Separable First-Order Equations 4 Separable First-Order Equations As we will see below, the notion of a differential equation being separable is a natural generalization of the notion of a first-order differential equation being directly

More information

Roberto s Notes on Linear Algebra Chapter 9: Orthogonality Section 2. Orthogonal matrices

Roberto s Notes on Linear Algebra Chapter 9: Orthogonality Section 2. Orthogonal matrices Roberto s Notes on Linear Algebra Chapter 9: Orthogonality Section 2 Orthogonal matrices What you need to know already: What orthogonal and orthonormal bases for subspaces are. What you can learn here:

More information

a factors The exponential 0 is a special case. If b is any nonzero real number, then

a factors The exponential 0 is a special case. If b is any nonzero real number, then 0.1 Exponents The expression x a is an exponential expression with base x and exponent a. If the exponent a is a positive integer, then the expression is simply notation that counts how many times the

More information

X. Numerical Methods

X. Numerical Methods X. Numerical Methods. Taylor Approximation Suppose that f is a function defined in a neighborhood of a point c, and suppose that f has derivatives of all orders near c. In section 5 of chapter 9 we introduced

More information

Vectors for Beginners

Vectors for Beginners Vectors for Beginners Leo Dorst September 6, 2007 1 Three ways of looking at linear algebra We will always try to look at what we do in linear algebra at three levels: geometric: drawing a picture. This

More information

Math 1270 Honors ODE I Fall, 2008 Class notes # 14. x 0 = F (x; y) y 0 = G (x; y) u 0 = au + bv = cu + dv

Math 1270 Honors ODE I Fall, 2008 Class notes # 14. x 0 = F (x; y) y 0 = G (x; y) u 0 = au + bv = cu + dv Math 1270 Honors ODE I Fall, 2008 Class notes # 1 We have learned how to study nonlinear systems x 0 = F (x; y) y 0 = G (x; y) (1) by linearizing around equilibrium points. If (x 0 ; y 0 ) is an equilibrium

More information

1 Continuity and Limits of Functions

1 Continuity and Limits of Functions Week 4 Summary This week, we will move on from our discussion of sequences and series to functions. Even though sequences and functions seem to be very different things, they very similar. In fact, we

More information

Lesson 3: Solving Equations A Balancing Act

Lesson 3: Solving Equations A Balancing Act Opening Exercise Let s look back at the puzzle in Lesson 1 with the t-shape and the 100-chart. Jennie came up with a sum of 380 and through the lesson we found that the expression to represent the sum

More information

Computationally, diagonal matrices are the easiest to work with. With this idea in mind, we introduce similarity:

Computationally, diagonal matrices are the easiest to work with. With this idea in mind, we introduce similarity: Diagonalization We have seen that diagonal and triangular matrices are much easier to work with than are most matrices For example, determinants and eigenvalues are easy to compute, and multiplication

More information

Answers in blue. If you have questions or spot an error, let me know. 1. Find all matrices that commute with A =. 4 3

Answers in blue. If you have questions or spot an error, let me know. 1. Find all matrices that commute with A =. 4 3 Answers in blue. If you have questions or spot an error, let me know. 3 4. Find all matrices that commute with A =. 4 3 a b If we set B = and set AB = BA, we see that 3a + 4b = 3a 4c, 4a + 3b = 3b 4d,

More information

EXAMPLES OF PROOFS BY INDUCTION

EXAMPLES OF PROOFS BY INDUCTION EXAMPLES OF PROOFS BY INDUCTION KEITH CONRAD 1. Introduction In this handout we illustrate proofs by induction from several areas of mathematics: linear algebra, polynomial algebra, and calculus. Becoming

More information

7.5 Partial Fractions and Integration

7.5 Partial Fractions and Integration 650 CHPTER 7. DVNCED INTEGRTION TECHNIQUES 7.5 Partial Fractions and Integration In this section we are interested in techniques for computing integrals of the form P(x) dx, (7.49) Q(x) where P(x) and

More information

1 Dirac Notation for Vector Spaces

1 Dirac Notation for Vector Spaces Theoretical Physics Notes 2: Dirac Notation This installment of the notes covers Dirac notation, which proves to be very useful in many ways. For example, it gives a convenient way of expressing amplitudes

More information

Final Review Sheet. B = (1, 1 + 3x, 1 + x 2 ) then 2 + 3x + 6x 2

Final Review Sheet. B = (1, 1 + 3x, 1 + x 2 ) then 2 + 3x + 6x 2 Final Review Sheet The final will cover Sections Chapters 1,2,3 and 4, as well as sections 5.1-5.4, 6.1-6.2 and 7.1-7.3 from chapters 5,6 and 7. This is essentially all material covered this term. Watch

More information

Matrices. 1 a a2 1 b b 2 1 c c π

Matrices. 1 a a2 1 b b 2 1 c c π Matrices 2-3-207 A matrix is a rectangular array of numbers: 2 π 4 37 42 0 3 a a2 b b 2 c c 2 Actually, the entries can be more general than numbers, but you can think of the entries as numbers to start

More information

Algebra Exam. Solutions and Grading Guide

Algebra Exam. Solutions and Grading Guide Algebra Exam Solutions and Grading Guide You should use this grading guide to carefully grade your own exam, trying to be as objective as possible about what score the TAs would give your responses. Full

More information

Matrices. Ellen Kulinsky

Matrices. Ellen Kulinsky Matrices Ellen Kulinsky To learn the most (AKA become the smartest): Take notes. This is very important! I will sometimes tell you what to write down, but usually you will need to do it on your own. I

More information

Numerical Methods Lecture 2 Simultaneous Equations

Numerical Methods Lecture 2 Simultaneous Equations Numerical Methods Lecture 2 Simultaneous Equations Topics: matrix operations solving systems of equations pages 58-62 are a repeat of matrix notes. New material begins on page 63. Matrix operations: Mathcad

More information

Chapter 7 Rational Expressions, Equations, and Functions

Chapter 7 Rational Expressions, Equations, and Functions Chapter 7 Rational Expressions, Equations, and Functions Section 7.1: Simplifying, Multiplying, and Dividing Rational Expressions and Functions Section 7.2: Adding and Subtracting Rational Expressions

More information

Lecture 6: Finite Fields

Lecture 6: Finite Fields CCS Discrete Math I Professor: Padraic Bartlett Lecture 6: Finite Fields Week 6 UCSB 2014 It ain t what they call you, it s what you answer to. W. C. Fields 1 Fields In the next two weeks, we re going

More information

) (d o f. For the previous layer in a neural network (just the rightmost layer if a single neuron), the required update equation is: 2.

) (d o f. For the previous layer in a neural network (just the rightmost layer if a single neuron), the required update equation is: 2. 1 Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.034 Artificial Intelligence, Fall 2011 Recitation 8, November 3 Corrected Version & (most) solutions

More information

MATHEMATICS FOR ENGINEERS & SCIENTISTS 23

MATHEMATICS FOR ENGINEERS & SCIENTISTS 23 MATHEMATICS FOR ENGINEERS & SCIENTISTS 3.5. Second order linear O.D.E.s: non-homogeneous case.. We ll now consider non-homogeneous second order linear O.D.E.s. These are of the form a + by + c rx) for

More information

Math 334 A1 Homework 3 (Due Nov. 5 5pm)

Math 334 A1 Homework 3 (Due Nov. 5 5pm) Math 334 A1 Homework 3 Due Nov. 5 5pm No Advanced or Challenge problems will appear in homeworks. Basic Problems Problem 1. 4.1 11 Verify that the given functions are solutions of the differential equation,

More information

Math 192r, Problem Set #3: Solutions

Math 192r, Problem Set #3: Solutions Math 192r Problem Set #3: Solutions 1. Let F n be the nth Fibonacci number as Wilf indexes them (with F 0 F 1 1 F 2 2 etc.). Give a simple homogeneous linear recurrence relation satisfied by the sequence

More information

Math 480 The Vector Space of Differentiable Functions

Math 480 The Vector Space of Differentiable Functions Math 480 The Vector Space of Differentiable Functions The vector space of differentiable functions. Let C (R) denote the set of all infinitely differentiable functions f : R R. Then C (R) is a vector space,

More information

Linear Independence Reading: Lay 1.7

Linear Independence Reading: Lay 1.7 Linear Independence Reading: Lay 17 September 11, 213 In this section, we discuss the concept of linear dependence and independence I am going to introduce the definitions and then work some examples and

More information

The symmetric group R + :1! 2! 3! 1. R :1! 3! 2! 1.

The symmetric group R + :1! 2! 3! 1. R :1! 3! 2! 1. Chapter 2 The symmetric group Consider the equilateral triangle. 3 1 2 We want to describe all the symmetries, which are the motions (both rotations and flips) which takes the triangle to itself. First

More information

Linear algebra and differential equations (Math 54): Lecture 20

Linear algebra and differential equations (Math 54): Lecture 20 Linear algebra and differential equations (Math 54): Lecture 20 Vivek Shende April 7, 2016 Hello and welcome to class! Last time We started discussing differential equations. We found a complete set of

More information

Work sheet / Things to know. Chapter 3

Work sheet / Things to know. Chapter 3 MATH 251 Work sheet / Things to know 1. Second order linear differential equation Standard form: Chapter 3 What makes it homogeneous? We will, for the most part, work with equations with constant coefficients

More information

Example. The 0 transformation Given any vector spaces V and W, the transformation

Example. The 0 transformation Given any vector spaces V and W, the transformation Unit 3, Section : Linear Transformations Linear Transformations of Vector Spaces As we begin to think more deeply about the structure of vector spaces, our next logical step is to understand vector spaces

More information

MATH Max-min Theory Fall 2016

MATH Max-min Theory Fall 2016 MATH 20550 Max-min Theory Fall 2016 1. Definitions and main theorems Max-min theory starts with a function f of a vector variable x and a subset D of the domain of f. So far when we have worked with functions

More information

Problem 1 (Equations with the dependent variable missing) By means of the substitutions. v = dy dt, dv

Problem 1 (Equations with the dependent variable missing) By means of the substitutions. v = dy dt, dv V Problem 1 (Equations with the dependent variable missing) By means of the substitutions v = dy dt, dv dt = d2 y dt 2 solve the following second-order differential equations 1. t 2 d2 y dt + 2tdy 1 =

More information

A Brief Review of Elementary Ordinary Differential Equations

A Brief Review of Elementary Ordinary Differential Equations A A Brief Review of Elementary Ordinary Differential Equations At various points in the material we will be covering, we will need to recall and use material normally covered in an elementary course on

More information

Rolle s Theorem. The theorem states that if f (a) = f (b), then there is at least one number c between a and b at which f ' (c) = 0.

Rolle s Theorem. The theorem states that if f (a) = f (b), then there is at least one number c between a and b at which f ' (c) = 0. Rolle s Theorem Rolle's Theorem guarantees that there will be at least one extreme value in the interior of a closed interval, given that certain conditions are satisfied. As with most of the theorems

More information

Vectors Year 12 Term 1

Vectors Year 12 Term 1 Vectors Year 12 Term 1 1 Vectors - A Vector has Two properties Magnitude and Direction - A vector is usually denoted in bold, like vector a, or a, or many others. In 2D - a = xı + yȷ - a = x, y - where,

More information

15-251: Great Theoretical Ideas In Computer Science Recitation 9 : Randomized Algorithms and Communication Complexity Solutions

15-251: Great Theoretical Ideas In Computer Science Recitation 9 : Randomized Algorithms and Communication Complexity Solutions 15-251: Great Theoretical Ideas In Computer Science Recitation 9 : Randomized Algorithms and Communication Complexity Solutions Definitions We say a (deterministic) protocol P computes f if (x, y) {0,

More information

Math 361: Homework 1 Solutions

Math 361: Homework 1 Solutions January 3, 4 Math 36: Homework Solutions. We say that two norms and on a vector space V are equivalent or comparable if the topology they define on V are the same, i.e., for any sequence of vectors {x

More information

is any vector v that is a sum of scalar multiples of those vectors, i.e. any v expressible as v = c 1 v n ... c n v 2 = 0 c 1 = c 2

is any vector v that is a sum of scalar multiples of those vectors, i.e. any v expressible as v = c 1 v n ... c n v 2 = 0 c 1 = c 2 Math 225-4 Week 8 Finish sections 42-44 and linear combination concepts, and then begin Chapter 5 on linear differential equations, sections 5-52 Mon Feb 27 Use last Friday's notes to talk about linear

More information

ENGINEERING MATH 1 Fall 2009 VECTOR SPACES

ENGINEERING MATH 1 Fall 2009 VECTOR SPACES ENGINEERING MATH 1 Fall 2009 VECTOR SPACES A vector space, more specifically, a real vector space (as opposed to a complex one or some even stranger ones) is any set that is closed under an operation of

More information

29. GREATEST COMMON FACTOR

29. GREATEST COMMON FACTOR 29. GREATEST COMMON FACTOR Don t ever forget what factoring is all about! greatest common factor a motivating example: cutting three boards of different lengths into same-length pieces solving the problem:

More information

APPM 2360 Section Exam 3 Wednesday November 19, 7:00pm 8:30pm, 2014

APPM 2360 Section Exam 3 Wednesday November 19, 7:00pm 8:30pm, 2014 APPM 2360 Section Exam 3 Wednesday November 9, 7:00pm 8:30pm, 204 ON THE FRONT OF YOUR BLUEBOOK write: () your name, (2) your student ID number, (3) lecture section, (4) your instructor s name, and (5)

More information

Getting Started with Communications Engineering

Getting Started with Communications Engineering 1 Linear algebra is the algebra of linear equations: the term linear being used in the same sense as in linear functions, such as: which is the equation of a straight line. y ax c (0.1) Of course, if we

More information

3.5 Quadratic Approximation and Convexity/Concavity

3.5 Quadratic Approximation and Convexity/Concavity 3.5 Quadratic Approximation and Convexity/Concavity 55 3.5 Quadratic Approximation and Convexity/Concavity Overview: Second derivatives are useful for understanding how the linear approximation varies

More information

Homework 3 Solutions Math 309, Fall 2015

Homework 3 Solutions Math 309, Fall 2015 Homework 3 Solutions Math 39, Fall 25 782 One easily checks that the only eigenvalue of the coefficient matrix is λ To find the associated eigenvector, we have 4 2 v v 8 4 (up to scalar multiplication)

More information

4.3 - Linear Combinations and Independence of Vectors

4.3 - Linear Combinations and Independence of Vectors - Linear Combinations and Independence of Vectors De nitions, Theorems, and Examples De nition 1 A vector v in a vector space V is called a linear combination of the vectors u 1, u,,u k in V if v can be

More information

Linear algebra and differential equations (Math 54): Lecture 7

Linear algebra and differential equations (Math 54): Lecture 7 Linear algebra and differential equations (Math 54): Lecture 7 Vivek Shende February 9, 2016 Hello and welcome to class! Last time We introduced linear subspaces and bases. Today We study the determinant

More information

Chapter 4: Higher-Order Differential Equations Part 1

Chapter 4: Higher-Order Differential Equations Part 1 Chapter 4: Higher-Order Differential Equations Part 1 王奕翔 Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw October 8, 2013 Higher-Order Differential Equations Most of this

More information

and lim lim 6. The Squeeze Theorem

and lim lim 6. The Squeeze Theorem Limits (day 3) Things we ll go over today 1. Limits of the form 0 0 (continued) 2. Limits of piecewise functions 3. Limits involving absolute values 4. Limits of compositions of functions 5. Limits similar

More information

Second Order and Higher Order Equations Introduction

Second Order and Higher Order Equations Introduction Second Order and Higher Order Equations Introduction Second order and higher order equations occur frequently in science and engineering (like pendulum problem etc.) and hence has its own importance. It

More information

ES.1803 Topic 7 Notes Jeremy Orloff. 7 Solving linear DEs; Method of undetermined coefficients

ES.1803 Topic 7 Notes Jeremy Orloff. 7 Solving linear DEs; Method of undetermined coefficients ES.1803 Topic 7 Notes Jeremy Orloff 7 Solving linear DEs; Method of undetermined coefficients 7.1 Goals 1. Be able to solve a linear differential equation by superpositioning a particular solution with

More information

Contents. 6 Systems of First-Order Linear Dierential Equations. 6.1 General Theory of (First-Order) Linear Systems

Contents. 6 Systems of First-Order Linear Dierential Equations. 6.1 General Theory of (First-Order) Linear Systems Dierential Equations (part 3): Systems of First-Order Dierential Equations (by Evan Dummit, 26, v 2) Contents 6 Systems of First-Order Linear Dierential Equations 6 General Theory of (First-Order) Linear

More information

DIFFERENTIAL EQUATIONS REVIEW. Here are notes to special make-up discussion 35 on November 21, in case you couldn t make it.

DIFFERENTIAL EQUATIONS REVIEW. Here are notes to special make-up discussion 35 on November 21, in case you couldn t make it. DIFFERENTIAL EQUATIONS REVIEW PEYAM RYAN TABRIZIAN Here are notes to special make-up discussion 35 on November 21, in case you couldn t make it. Welcome to the special Friday after-school special of That

More information

MATH 319, WEEK 2: Initial Value Problems, Existence/Uniqueness, First-Order Linear DEs

MATH 319, WEEK 2: Initial Value Problems, Existence/Uniqueness, First-Order Linear DEs MATH 319, WEEK 2: Initial Value Problems, Existence/Uniqueness, First-Order Linear DEs 1 Initial-Value Problems We have seen that differential equations can, in general, given rise to multiple solutions.

More information

Solving Differential Equations: First Steps

Solving Differential Equations: First Steps 30 ORDINARY DIFFERENTIAL EQUATIONS 3 Solving Differential Equations Solving Differential Equations: First Steps Now we start answering the question which is the theme of this book given a differential

More information

Chapter 11 - Sequences and Series

Chapter 11 - Sequences and Series Calculus and Analytic Geometry II Chapter - Sequences and Series. Sequences Definition. A sequence is a list of numbers written in a definite order, We call a n the general term of the sequence. {a, a

More information

Designing Information Devices and Systems I Spring 2018 Lecture Notes Note Introduction to Linear Algebra the EECS Way

Designing Information Devices and Systems I Spring 2018 Lecture Notes Note Introduction to Linear Algebra the EECS Way EECS 16A Designing Information Devices and Systems I Spring 018 Lecture Notes Note 1 1.1 Introduction to Linear Algebra the EECS Way In this note, we will teach the basics of linear algebra and relate

More information

Linear Algebra (part 1) : Matrices and Systems of Linear Equations (by Evan Dummit, 2016, v. 2.02)

Linear Algebra (part 1) : Matrices and Systems of Linear Equations (by Evan Dummit, 2016, v. 2.02) Linear Algebra (part ) : Matrices and Systems of Linear Equations (by Evan Dummit, 206, v 202) Contents 2 Matrices and Systems of Linear Equations 2 Systems of Linear Equations 2 Elimination, Matrix Formulation

More information

Lecture 3q Bases for Row(A), Col(A), and Null(A) (pages )

Lecture 3q Bases for Row(A), Col(A), and Null(A) (pages ) Lecture 3q Bases for Row(A), Col(A), and Null(A) (pages 57-6) Recall that the basis for a subspace S is a set of vectors that both spans S and is linearly independent. Moreover, we saw in section 2.3 that

More information