Section 5.1 First-Order Systems & Applications

Slides:



Advertisements
Similar presentations
Ch 3.2: Solutions of Linear Homogeneous Equations; Wronskian
Advertisements

5.1 Real Vector Spaces.
Ch 7.7: Fundamental Matrices
Chapter 6 Eigenvalues and Eigenvectors
Section 6.1 Cauchy-Euler Equation. THE CAUCHY-EULER EQUATION Any linear differential equation of the from where a n,..., a 0 are constants, is said to.
Chapter 2: Second-Order Differential Equations
Boyce/DiPrima 10th ed, Ch 10.1: Two-Point Boundary Value Problems Elementary Differential Equations and Boundary Value Problems, 10th edition, by William.
Section 2.1 Introduction: Second-Order Linear Equations.
Section 3.1 The Determinant of a Matrix. Determinants are computed only on square matrices. Notation: det(A) or |A| For 1 x 1 matrices: det( [k] ) = k.
Second-Order Differential
Eigenvalues and Eigenvectors
Ch 3.5: Nonhomogeneous Equations; Method of Undetermined Coefficients
Ch 7.9: Nonhomogeneous Linear Systems
Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues
Ch 6.2: Solution of Initial Value Problems
Ch 7.1: Introduction to Systems of First Order Linear Equations
Math for CS Second Order Linear Differential Equations
Chapter 1 Systems of Linear Equations
Ordinary Differential Equations Final Review Shurong Sun University of Jinan Semester 1,
10.1 Gaussian Elimination Method
化工應用數學 授課教師: 郭修伯 Lecture 9 Matrices
Differential Equations
Boyce/DiPrima 9th ed, Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues Elementary Differential Equations and Boundary Value Problems,
Linear Equations in Linear Algebra
Sheng-Fang Huang. Introduction If r (x) = 0 (that is, r (x) = 0 for all x considered; read “r (x) is identically zero”), then (1) reduces to (2) y"
8.1 Vector spaces A set of vector is said to form a linear vector space V Chapter 8 Matrices and vector spaces.
Linear Algebra/Eigenvalues and eigenvectors. One mathematical tool, which has applications not only for Linear Algebra but for differential equations,
SECOND-ORDER DIFFERENTIAL EQUATIONS
1 On Free Mechanical Vibrations As derived in section 4.1( following Newton’s 2nd law of motion and the Hooke’s law), the D.E. for the mass-spring oscillator.
1 Part II: Linear Algebra Chapter 8 Systems of Linear Algebraic Equations; Gauss Elimination 8.1 Introduction There are many applications in science and.
Eigenvalues and Eigenvectors
Sections 1.8/1.9: Linear Transformations
Section 4.1 Vectors in ℝ n. ℝ n Vectors Vector addition Scalar multiplication.
Chapter 8 With Question/Answer Animations 1. Chapter Summary Applications of Recurrence Relations Solving Linear Recurrence Relations Homogeneous Recurrence.
6.5 Fundamental Matrices and the Exponential of a Matrix Fundamental Matrices Suppose that x 1 (t),..., x n (t) form a fundamental set of solutions for.
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.
Sheng-Fang Huang. 4.0 Basics of Matrices and Vectors Most of our linear systems will consist of two ODEs in two unknown functions y 1 (t), y 2 (t),
Computing Eigen Information for Small Matrices The eigen equation can be rearranged as follows: Ax = x  Ax = I n x  Ax - I n x = 0  (A - I n )x = 0.
Chapter 5 Eigenvalues and Eigenvectors 大葉大學 資訊工程系 黃鈴玲 Linear Algebra.
Differential Equations MTH 242 Lecture # 13 Dr. Manshoor Ahmed.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 02 Chapter 2: Determinants.
Chapter 5 MATRIX ALGEBRA: DETEMINANT, REVERSE, EIGENVALUES.
Homogeneous Linear Systems with Constant Coefficients Solutions of Systems of ODEs.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 07 Chapter 7: Eigenvalues, Eigenvectors.
Nonhomogeneous Linear Systems Undetermined Coefficients.
12/19/ Non- homogeneous Differential Equation Chapter 4.
Chapter 6 Eigenvalues. Example In a certain town, 30 percent of the married women get divorced each year and 20 percent of the single women get married.
Chapter 1 Systems of Linear Equations Linear Algebra.
5.1 Eigenvectors and Eigenvalues 5. Eigenvalues and Eigenvectors.
Math 3120 Differential Equations with Boundary Value Problems
Ch7: Linear Systems of Differential Equations
Differential Equations MTH 242 Lecture # 09 Dr. Manshoor Ahmed.
Linear Algebra Chapter 6 Linear Algebra with Applications -Gareth Williams Br. Joel Baumeyer, F.S.C.
5 5.1 © 2016 Pearson Education, Ltd. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
Ch 4.2: Homogeneous Equations with Constant Coefficients Consider the nth order linear homogeneous differential equation with constant, real coefficients:
Differential Equations MTH 242 Lecture # 28 Dr. Manshoor Ahmed.
Tutorial 6. Eigenvalues & Eigenvectors Reminder: Eigenvectors A vector x invariant up to a scaling by λ to a multiplication by matrix A is called.
Chapter 6 Eigenvalues and Eigenvectors
Systems of Linear Differential Equations
Linear Equations Constant Coefficients
Matrices and vector spaces
Ch 10.1: Two-Point Boundary Value Problems
Systems of First Order Linear Equations
MATH 374 Lecture 23 Complex Eigenvalues.
MAE 82 – Engineering Mathematics
Linear Algebra Lecture 32.
EIGENVECTORS AND EIGENVALUES
Homogeneous Linear Systems
Eigenvalues and Eigenvectors
Presentation transcript:

Section 5.1 First-Order Systems & Applications

Suppose x and y are both functions of t. Solve: x′ = 3x – y y′ = 2x + y – et

Why would we consider such things?

Ex. 1 Give the system of diff eqs which describe the following tanks containing brine solution:

Theorem: Consider the following system of diff eqs (all xi, pij, and fi are functions of t ) x1′ = p11x1 + p12x2 + p13x3 + ⋯ + p1nxn + f1 x2′ = p21x1 + p22x2 + p23x3 + ⋯ + p2nxn + f2 x3′ = p31x1 + p32x2 + p33x3 + ⋯ + p3nxn + f3 : : xn′ = pn1x1 + pn2x2 + pn3x3 + ⋯ + pnnxn + fn   Let J be an open interval containing t = a. Suppose the functions pij and the functions fk are continuous on J. Then the system of differential equations has a unique solution that satisfies the following initial conditions: x1(a) = b1 , x2(a) = b2 , x3(a) = b3 , ......... , xn(a) = bn

If we have a system of higher order diff eqs, we can transform it into a system of first order diff eqs.

Ex. 3 Rewrite the one diff eq x(3) + 3x″ + 2x′ – 5x = sin(2t) as a system of first order diff eqs.

Ex. 4 Rewrite the following system as a system of first order diff eqs. 2x″ = –6x + 2y y″ = 2x – 2y + 40sin(3t)

Section 5.2 The Method Of Elimination

Review of solving systems of algebraic equations (2 equations with 2 unknowns) i. Substitution method ii. Elimination method iii. Cramer's rule

Ex. 1 Find the general solution to the following system by using a variant of the substitution method. x′ = 4x – 3y y′ = 6x – 7y

Ex. 1 Find the general solution to the following system by using a variant of the substitution method. x′ = 4x – 3y y′ = 6x – 7y

Ex. 2 Find the general solution to the following system by using a variant of the elimination method. x′ = 4x – 3y y′ = 6x – 7y

Ex. 2 Find the general solution to the following system by using a variant of the elimination method. x′ = 4x – 3y y′ = 6x – 7y

We shall now use the following notation: L will be a linear operator of the form L = anDn + an–1Dn–1 + an–2Dn–2 + ⋯ + a2D2 + a1D + a0

Ex. 3 Find the general solution to the following general system of diff eqs: L1x + L2y = f1(t) L3x + L4y = f2(t)

Ex. 3 Find the general solution to the following general system of diff eqs: L1x + L2y = f1(t) L3x + L4y = f2(t)

Ex. 4 Find the general solution to x′ = 2x + y y′ = 2x + 3y + e5t   Solution: (D–2)x – y = 0 –2x + (D–3)y = e5t [(D–2)(D–3) – 2] x = (D – 3)0 + e5t [(D–2)(D–3) – 2] y = (D – 2) e5t + 2(0) (D2–5D+4) x = e5t (D2–5D+4) y = 5e5t – 2e5t (D–4)(D–1) x = e5t (D–4)(D–1) y = 3e5t xc = c1e4t + c2et yc = c3e4t + c4et xp = Ae5t ⇒ xp = (1∕4)e5t yp = Be5t ⇒ yp = (3∕4)e5t x = c1e4t + c2et + (1∕4)e5t y = c3e4t + c4et + (3∕4)e5t 

Ex. 4 Find the general solution to x′ = 2x + y y′ = 2x + 3y + e5t   Solution: x = c1e4t + c2et + (1∕4)e5t y = c3e4t + c4et + (3∕4)e5t Plugging these solutions into the first equation in the initial problem we get: x′ = 2x + y (c1e4t + c2et + (1∕4)e5t)′ = 2(c1e4t + c2et + (1∕4)e5t) + (c3e4t + c4et + (3∕4)e5t) 4c1e4t + c2et + (5/4)e5t = 2c1e4t + 2c2et + (1/2)e5t + c3e4t + c4et + (3/4)e5t 0 = (–2c1 + c3)e4t + (c2 + c4)et –2c1 + c3 = 0 ⇒ c3 = 2c1 c2 + c4 = 0 c4 = –c2

Section 5.3 Matrices & Linear Systems

x1′ = 2x1 + x2 x2′ = 2x1 + 3x2

x1′ = 2x1 + x2 ⇒ x2′ = 2x1 + 3x2

Previously we would state: The general solution to the system x1′ = 2x1 + x2 turns out to be: x1 = c1e4t + c2et x2′ = 2x1 + 3x2 x2 = 2c1e4t – c2et    

Previously we would state: The general solution to the system x1′ = 2x1 + x2 turns out to be: x1 = c1e4t + c2et x2′ = 2x1 + 3x2 x2 = 2c1e4t – c2et   We now would state: The general solution to the system turns out to be:  

Previously we would state: The general solution to the system x1′ = 2x1 + x2 turns out to be: x1 = c1e4t + c2et x2′ = 2x1 + 3x2 x2 = 2c1e4t – c2et   We now would state: The general solution to the system turns out to be:   We shall find that general solutions to these "2x2 homogenous systems" will take this form of (where and are two linearly independent vectors).

x1′ = p11x1 + p12x2 + p13x3 + ⋯ + p1nxn + f1 : : xn′ = pn1x1 + pn2x2 + pn3x3 + ⋯ + pnnxn + fn  

x1′ = p11x1 + p12x2 + p13x3 + ⋯ + p1nxn + f1 : : xn′ = pn1x1 + pn2x2 + pn3x3 + ⋯ + pnnxn + fn  

x1′ = p11x1 + p12x2 + p13x3 + ⋯ + p1nxn + f1 : : xn′ = pn1x1 + pn2x2 + pn3x3 + ⋯ + pnnxn + fn   This system of diff eqs is said to be homogenous if

x1′ = p11x1 + p12x2 + p13x3 + ⋯ + p1nxn + f1 : : xn′ = pn1x1 + pn2x2 + pn3x3 + ⋯ + pnnxn + fn   This system of diff eqs is said to be homogenous if Thus, the matrix equation would be for a homogenous system.

We shall now see many theorems very similar to theorems and definitions we had in chapter 2.

are said to be linearly independent if the equation Definition: are said to be linearly independent if the equation only has the solution of c1 = c2 = ⋯ = cn = 0.

Definition: If are solutions to then we define the Wronskian of these solution to be  

Theorem: Suppose are solutions to on an interval J where all the pij functions are continuous. (a) If are linearly dependent then W = 0 for every point on the interval J. (b) If are linearly independent then W ≠ 0 for every point on the interval J.

Theorem: Suppose are linearly independent solutions to on an interval J where all the pij functions are continuous. The general solution to is given as:

Theorem: Suppose is a particular solution to the nonhomogenous system and is the general solution to the corresponding homogenous system . Then the general solution to the nonhomogenous system is .

Ex. 1 x′1 = x1 + x2 – 2x3 x′2 = –x1 + 2x2 + x3 x′3 = x2 – x3   (a) Write this system as a matrix equation.

Ex. 1 x′1 = x1 + x2 – 2x3 x′2 = –x1 + 2x2 + x3 x′3 = x2 – x3   (b) Verify that the following three vectors are solution vectors:

Ex. 1 x′1 = x1 + x2 – 2x3 x′2 = –x1 + 2x2 + x3 x′3 = x2 – x3   (c) Verify that these are linearly independent vectors.

Ex. 1 x′1 = x1 + x2 – 2x3 x′2 = –x1 + 2x2 + x3 x′3 = x2 – x3   (d) Give the general solution for the system.

Ex. 1 x′1 = x1 + x2 – 2x3 x′2 = –x1 + 2x2 + x3 x′3 = x2 – x3   (e) Give the general solution for x1. Give the general solution for x2. Give the general solution for x3.

Ex. 1 x′1 = x1 + x2 – 2x3 x′2 = –x1 + 2x2 + x3 x′3 = x2 – x3   (f) Solve the initial value problem: x′1 = x1 + x2 – 2x3 x1(0) = 9, x2(0) = –2, x3(0) = 5 x′3 = x2 – x3

Section 5.4 The Eigenvalue Method for Homogeneous Systems

Review of eigenvalues and eigenvectors: Let A be a square matrix. The vector is said to be an eigenvector for the eigenvalue λ if .

Review of eigenvalues and eigenvectors: Let A be a square matrix. The vector is said to be an eigenvector for the eigenvalue λ if . What is the connection between eigenvectors and solutions to systems of diff eqs?

Ex. 1 We previously (in section 5 Ex. 1 We previously (in section 5.3) found three linearly independent solution vectors to the system One of these was Verify that this solution is an eigenvector of

Ex. 2 Suppose that (where A, B, C, and k are constants) is a solution vector to the system . Show that this solution vector is an eigenvector of the matrix P.

Theorem: Given a homogenous system , suppose λ is an eigenvalue of P with eigenvector . Then is a nontrivial solution vector to .

Ex. 3 Use eigenvalues/eigenvectors to find the general solution to the following system and write your final solution in scalar form. x′1 = 3x1 + x2 x′2 = 3x1 + 5x2

Ex. 3 Use eigenvalues/eigenvectors to find the general solution to the following system and write your final solution in scalar form. x′1 = 3x1 + x2 x′2 = 3x1 + 5x2

Note: Suppose where P is an nxn matrix. If there are n distinct real eigenvalues of P then we've got n linearly independent solution vectors. This means we can write down the general solution as (here the λi are the eigenvalues, the are the eigenvectors and the ci are arbitrary constants). If we have less than n distinct eigenvalues, or if some of the eigenvalues are complex then we will run into trouble and need to do something else.

Theorem: Given a homogenous system , suppose α + βi is a complex eigenvalue of P with eigenvector . Then the real and imaginary parts of the vector will form two linearly independent solution vectors.  

Theorem: Given a homogenous system , suppose α + βi is a complex eigenvalue of P with eigenvector . Then the real and imaginary parts of the vector will form two linearly independent solution vectors.   Note that there are formulas for these two vectors, but you should not use them! Instead of using these formulas we shall just use this theorem which indicates that we should find the one vector , then split it up into its real and imaginary parts.

Ex. 4 Use eigenvalues/eigenvectors to find the general solution to x′1 = –x1 – x2 x′2 = 4x1 – x2

Ex. 4 Use eigenvalues/eigenvectors to find the general solution to x′1 = –x1 – x2 x′2 = 4x1 – x2

Section 5.5 Multiple Eigenvalue Solutions

If we have less than n distinct eigenvalues (where P is an nxn matrix) then we may have a hard time finding enough linearly independent solution vectors. When an eigenvalue, say λ, is a repeated root with multiplicity k of the characteristic equation, then two possibilities arise:

If we have less than n distinct eigenvalues (where P is an nxn matrix) then we may have a hard time finding enough linearly independent solution vectors. When an eigenvalue, say λ, is a repeated root with multiplicity k of the characteristic equation, then two possibilities arise: 1. There are "just enough" linearly independent eigenvectors associated with . Then there are "just enough" solution vectors: for us to form the general solution to the diff eqs. 2. There are not enough linearly independent eigenvectors associated with λ. In this case λ is said to be defective.   We shall cover the first of these two cases.

Ex. 1 Use eigenvalues/eigenvectors to find the general solution to x′1 = –13x1 + 40x2 – 48x3 x′2 = –8x1 + 23x2 – 24x3 x′3 = 3x3

Ex. 1 Use eigenvalues/eigenvectors to find the general solution to x′1 = –13x1 + 40x2 – 48x3 x′2 = –8x1 + 23x2 – 24x3 x′3 = 3x3