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  1. B PART Linear Algebra. Vector Calculus Part B p1

  2. 8 CHAPTER Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p2

  3. 8.0Linear Algebra: Matrix Eigenvalue Problems A matrix eigenvalue problem considers the vector equation (1) Ax = λx. Here A is a given square matrix, λ an unknown scalar, and x an unknown vector. In a matrix eigenvalue problem, the task is to determine λ’s and x’s that satisfy (1). Since x = 0 is always a solution for any and thus not interesting, we only admit solutions with x ≠ 0. The solutions to (1) are given the following names: The λ’s that satisfy (1) are called eigenvalues of A and the corresponding nonzero x’s that also satisfy (1) are called eigenvectors of A. Section 8.0 p3

  4. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors Section 8.1 p4

  5. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors We formalize our observation. Let A = [ajk] be a given nonzero square matrix of dimension n ×n. Consider the following vector equation: (1) Ax = λx. The problem of finding nonzero x’s and λ’s that satisfy equation (1) is called an eigenvalue problem. Section 8.1 p5

  6. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors We introduce more terminology. A value of λ for which (1) has a solution x ≠ 0 is called an eigenvalue or characteristic value of the matrix A. Another term for λ is a latent root. (“Eigen” is German and means “proper” or “characteristic.”) The corresponding solutions x ≠ 0 of (1) are called the eigenvectors or characteristic vectors of A corresponding to that eigenvalue λ. The set of all the eigenvalues of A is called the spectrum of A. We shall see that the spectrum consists of at least one eigenvalue and at most of n numerically different eigenvalues. The largest of the absolute values of the eigenvalues of A is called the spectral radius of A, a name to be motivated later. Section 8.1 p6

  7. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors How to Find Eigenvalues and Eigenvectors EXAMPLE 1 Determination of Eigenvalues and Eigenvectors We illustrate all the steps in terms of the matrix Section 8.1 p7

  8. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 1) Determination of Eigenvalues and Eigenvectors Solution. (a) Eigenvalues. These must be determined first. Equation (1) is in components Section 8.1 p8

  9. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 2) Determination of Eigenvalues and Eigenvectors Solution. (continued 1) (a) Eigenvalues. (continued 1) Transferring the terms on the right to the left, we get (2*) This can be written in matrix notation (3*) Because (1) is Ax − λx = Ax − λIx = (A − λI)x = 0, which gives (3*). Section 8.1 p9

  10. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 3) Determination of Eigenvalues and Eigenvectors Solution. (continued 2) (a) Eigenvalues. (continued 2) We see that this is a homogeneous linear system. By Cramer’s theorem in Sec. 7.7 it has a nontrivial solution (an eigenvector of A we are looking for) if and only if its coefficient determinant is zero, that is, (4*) Section 8.1 p10

  11. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 4) Determination of Eigenvalues and Eigenvectors Solution. (continued 3) (a) Eigenvalues. (continued 3) We call D(λ) the characteristic determinant or, if expanded, the characteristic polynomial, and D(λ) = 0 the characteristic equation of A. The solutions of this quadratic equation are λ1 = −1 and λ2 = −6. These are the eigenvalues of A. (b1) Eigenvector of A corresponding to λ1. This vector is obtained from (2*) with λ = λ1 = −1, that is, Section 8.1 p11

  12. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 5) Determination of Eigenvalues and Eigenvectors Solution. (continued 4) (b1) Eigenvector of A corresponding to λ1. (continued) A solution is x2 = 2x1, as we see from either of the two equations, so that we need only one of them. This determines an eigenvector corresponding to λ1 = −1 up to a scalar multiple. If we choose x1 = 1, we obtain the eigenvector Section 8.1 p12

  13. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 6) Determination of Eigenvalues and Eigenvectors Solution. (continued 5) (b2) Eigenvector of A corresponding to λ2. For λ = λ2 = −6, equation (2*) becomes A solution is x2 = −x1/2 with arbitrary x1. If we choose x1 = 2, we get x2 = −1. Thus an eigenvector of A corresponding to λ2 = −6 is Section 8.1 p13

  14. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 1 (continued 7) Determination of Eigenvalues and Eigenvectors Solution. (continued 6) (b2) Eigenvector of A corresponding to λ2. (continued) For the matrix in the intuitive opening example at the start of Sec. 8.1, the characteristic equation is λ2 − 13λ + 30 = (λ −10)(λ − 3) = 0. The eigenvalues are {10, 3}. Corresponding eigenvectors are [3 4]T and [−1 1]T, respectively. The reader may want to verify this. Section 8.1 p14

  15. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors This example illustrates the general case as follows. Equation (1) written in components is Transferring the terms on the right side to the left side, we have (2) Section 8.1 p15

  16. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors In matrix notation, (3) By Cramer’s theorem in Sec. 7.7, this homogeneous linear system of equations has a nontrivial solution if and only if the corresponding determinant of the coefficients is zero: (4) Section 8.1 p16

  17. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors A −λI is called the characteristic matrix and D(λ) the characteristic determinant of A. Equation (4) is called the characteristic equation of A. By developing D(λ) we obtain a polynomial of nth degree in λ. This is called the characteristic polynomial of A. Section 8.1 p17

  18. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors Theorem 1 Eigenvalues The eigenvalues of a square matrix A are the roots of the characteristic equation (4) of A. Hence an n ×n matrix has at least one eigenvalue and at most n numerically different eigenvalues. Section 8.1 p18

  19. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors The eigenvalues must be determined first. Once these are known, corresponding eigenvectors are obtained from the system (2), for instance, by the Gauss elimination, where λis the eigenvalue for which an eigenvector is wanted. Section 8.1 p19

  20. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors Theorem 2 Eigenvectors, Eigenspace If w and x are eigenvectors of a matrix A corresponding to the same eigenvalue λ, so are w + x (provided x ≠−w) and kx for any k ≠ 0. Hence the eigenvectors corresponding to one and the same eigenvalue λ of A, together with 0, form a vector space (cf. Sec. 7.4), called the eigenspace of A corresponding to that λ. Section 8.1 p20

  21. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors In particular, an eigenvector x is determined only up to a constant factor. Hence we can normalize x, that is, multiply it by a scalar to get a unit vector (see Sec. 7.9). Section 8.1 p21

  22. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 2 Multiple Eigenvalues Find the eigenvalues and eigenvectors of Section 8.1 p22

  23. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 2 Multiple Eigenvalues (continued 1) Solution. For our matrix, the characteristic determinant gives the characteristic equation −λ3 − λ2 + 21λ + 45 = 0. The roots (eigenvalues of A) are λ1 = 5, λ2 = λ3 = −3. (If you have trouble finding roots, you may want to use a root finding algorithm such as Newton’s method (Sec. 19.2). Your CAS or scientific calculator can find roots. However, to really learn and remember this material, you have to do some exercises with paper and pencil.) Section 8.1 p23

  24. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 2 Multiple Eigenvalues (continued 2) Solution. (continued 1) To find eigenvectors, we apply the Gauss elimination (Sec. 7.3) to the system (A −λI)x = 0, first with λ = 5 and then with λ = −3 . For λ = 5 the characteristic matrix is It row-reduces to Section 8.1 p24

  25. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 2 Multiple Eigenvalues (continued 3) Solution. (continued 2) Hence it has rank 2. Choosing x3 = −1 we have x2 = 2 from and then x1 = 1 from −7x1 + 2x2 − 3x3 = 0. Hence an eigenvector of A corresponding to λ = 5 is x1 = [1 2 −1]T. For λ = −3 the characteristic matrix row-reduces to Section 8.1 p25

  26. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors EXAMPLE 2 Multiple Eigenvalues (continued 4) Solution. (continued 3) Hence it has rank 1. From x1 + 2x2 − 3x3 = 0 we have x1 = −2x2 + 3x3. Choosing x2 = 1, x3 = 0 and x2 = 0, x3 = 1, we obtain two linearly independent eigenvectors of A corresponding to λ = −3 [as they must exist by (5), Sec. 7.5, with rank = 1 and n = 3], Section 8.1 p26

  27. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors The order Mλ of an eigenvalue λ as a root of the characteristic polynomial is called the algebraic multiplicity of λ. The number mλ of linearly independent eigenvectors corresponding to λ is called the geometric multiplicity of λ. Thus mλ is the dimension of the eigenspace corresponding to this λ. Since the characteristic polynomial has degree n, the sum of all the algebraic multiplicities must equal n. In Example 2 for λ = −3 we have mλ = Mλ = 2. In general, mλ ≤ Mλ, as can be shown. The difference Δλ = Mλ − mλ is called the defect of λ. Thus Δ−3 = 0 in Example 2, but positive defects Δλ can easily occur. Section 8.1 p27

  28. 8.1The Matrix Eigenvalue Problem. Determining Eigenvalues and Eigenvectors Theorem 3 Eigenvalues of the Transpose The transpose ATof a square matrix A has the same eigenvalues as A. Section 8.1 p28

  29. 8.2Some Applications of Eigenvalue Problems Section 8.2 p29

  30. 8.2Some Applications of Eigenvalue Problems EXAMPLE 1 Stretching of an Elastic Membrane An elastic membrane in the x1x2-plane with boundary circle x12 + x22 = 1 (Fig. 160) is stretched so that a point P: (x1, x2) goes over into the point Q: (y1, y2) given by (1) in components, Find the principal directions, that is, the directions of the position vector x of P for which the direction of the position vector y of Q is the same or exactly opposite. What shape does the boundary circle take under this deformation? Section 8.2 p30

  31. 8.2Some Applications of Eigenvalue Problems EXAMPLE 1 (continued 1) Stretching of an Elastic Membrane Solution. We are looking for vectors x such that y = λx. Since y = Ax, this gives Ax = λx, the equation of an eigenvalue problem. In components, Ax = λx is (2) or The characteristic equation is (3) Section 8.2 p31

  32. 8.2Some Applications of Eigenvalue Problems EXAMPLE 1 (continued 2) Stretching of an Elastic Membrane Solution. (continued 1) Its solutions are λ1 = 8 and λ2 = 2. These are the eigenvalues of our problem. For λ1 = λ2 = 8, our system (2) becomes −3x1 + 3x2 = 0, Solution x2 = x1, x1 arbitrary, 3x1 − 3x2 = 0. for instance, x1 = x2 = 1. For λ2 = 2, our system (2) becomes 3x1 + 3x2 = 0, Solution x2 = −x1, x1 arbitrary, 3x1 + 3x2 = 0. for instance, x1 = 1, x2 = −1. Section 8.2 p32

  33. 8.2Some Applications of Eigenvalue Problems EXAMPLE 1 (continued 3) Stretching of an Elastic Membrane Solution. (continued 2) We thus obtain as eigenvectors of A, for instance, [1 1]T corresponding to λ1 and [1 −1]T corresponding to λ2 (or a nonzero scalar multiple of these). These vectors make 45°and 135°angles with the positive x1-direction. They give the principal directions, the answer to our problem. The eigenvalues show that in the principal directions the membrane is stretched by factors 8 and 2, respectively; see Fig. 160. Section 8.2 p33

  34. 8.2Some Applications of Eigenvalue Problems EXAMPLE 1 (continued 4) Stretching of an Elastic Membrane Solution. (continued 3) Accordingly, if we choose the principal directions as directions of a new Cartesian u1u2 coordinate system, say, with the positive u1-semi-axis in the first quadrant and the positive u2-semi-axis in the second quadrant of the x1x2-system, and if we set u1 = r cos φ, u2 = r sin φ, then a boundary point of the unstretched circular membrane has coordinates cos φ, sin φ. Hence, after the stretch we have z1 = 8 cos φ , z2 = 2 sin φ . Section 8.2 p34

  35. 8.2Some Applications of Eigenvalue Problems EXAMPLE 1 (continued 5) Stretching of an Elastic Membrane Solution. (continued 4) Since cos2φ + sin2φ= 1, this shows that the deformed boundary is an ellipse (Fig. 160) (4) Fig. 160.Undeformed and deformed membrane in Example 1 Section 8.2 p35

  36. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Section 8.3 p36

  37. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Definitions Symmetric, Skew-Symmetric, and Orthogonal Matrices A real square matrix A = [ajk] is called symmetric if transposition leaves it unchanged, (1) AT = A, thus akj= ajk, skew-symmetric if transposition gives the negative of A, (2) AT = −A, thus akj= −ajk, orthogonal if transposition gives the inverse of A, (3) AT = A−1. Section 8.3 p37

  38. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Any real square matrix A may be written as the sum of a symmetric matrix R and a skew-symmetric matrix S, where (4) Section 8.3 p38

  39. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Theorem 1 Eigenvalues of Symmetric and Skew-Symmetric Matrices (a) The eigenvalues of a symmetric matrix are real. (b) The eigenvalues of a skew-symmetric matrix are pure imaginary or zero. Section 8.3 p39

  40. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Orthogonal Transformations and Orthogonal Matrices Orthogonal transformations are transformations (5) y = Ax where A is an orthogonal matrix. With each vector x in Rnsuch a transformation assigns a vector y in Rn. For instance, the plane rotation through an angle θ (6) is an orthogonal transformation. Section 8.3 p40

  41. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Orthogonal Transformations and Orthogonal Matrices (continued) It can be shown that any orthogonal transformation in the plane or in three-dimensional space is a rotation (possibly combined with a reflection in a straight line or a plane, respectively). The main reason for the importance of orthogonal matrices is as follows. Section 8.3 p41

  42. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Theorem 2 Invariance of Inner Product An orthogonal transformation preserves the value of the inner product of vectors a and b in Rn, defined by (7) That is, for any a and b in Rn, orthogonal n ×n matrix A, and u = Aa, v = Ab we have u ·v = a ·b. Hence the transformation also preserves the length or norm of any vector a in Rn given by (8) Section 8.3 p42

  43. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Theorem 3 Orthonormality of Column and Row Vectors A real square matrix is orthogonal if and only if its column vectors a1, … , an (and also its row vectors) form an orthonormal system, that is, (10) Section 8.3 p43

  44. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Theorem 4 Determinant of an Orthogonal Matrix The determinant of an orthogonal matrix has the value +1 or −1. Section 8.3 p44

  45. 8.3Symmetric, Skew-Symmetric, and Orthogonal Matrices Theorem 5 Eigenvalues of an Orthogonal Matrix The eigenvalues of an orthogonal matrix A are real or complex conjugates in pairs and have absolute value 1. Section 8.3 p45

  46. 8.4Eigenbases. Diagonalization. Quadratic Forms Section 8.4 p46

  47. 8.4Eigenbases. Diagonalization. Quadratic Forms Eigenvectors of an n × n matrix A may (or may not!) form a basis for Rn. If we are interested in a transformation y =Ax, such an “eigenbasis” (basis of eigenvectors)—if it exists—is of great advantage because then we can represent any x in Rn uniquely as a linear combination of the eigenvectors x1, … , xn, say, x = c1x1 + c2x2 + … + cnxn. Section 8.4 p47

  48. 8.4Eigenbases. Diagonalization. Quadratic Forms And, denoting the corresponding (not necessarily distinct) eigenvalues of the matrix A by λ1, … , λn, we have Axj= λjxj, so that we simply obtain (1) This shows that we have decomposed the complicated action of A on an arbitrary vector x into a sum of simple actions (multiplication by scalars) on the eigenvectors of A. This is the point of an eigenbasis. Section 8.4 p48

  49. 8.4Eigenbases. Diagonalization. Quadratic Forms Theorem 1 Basis of Eigenvectors If an n × n matrix A has n distinct eigenvalues, then A has a basis of eigenvectors x1, … , xn for Rn. Section 8.4 p49

  50. 8.4Eigenbases. Diagonalization. Quadratic Forms Theorem 2 Symmetric Matrices A symmetric matrix has an orthonormal basis of eigenvectors for Rn. Section 8.4 p50