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Unformatted text preview: Lecture Note 101: March 26, 2007 Dr. Jeff ChakFu WONG Department of Mathematics Chinese University of Hong Kong [email protected] MAT 2310C Linear Algebra and Its Applications Spring, 2007 Produced by Jeff ChakFu WONG 1 E IGNEVALUES , E IGENVECTORS , AND D IAGONALIZATION 1. Eignevalues and Eigenvectors 2. Diagonalization 3. Diagonalization of Symmetric Matrices EIGNEVALUES, EIGENVECTORS, AND DIAGONALIZATION 2 E IGNEVALUES AND E IGENVECTORS In this lecture, every matrix considered is a square matrix . Let A be an n × n matrix. Then, as we have seen in Lecture note 6 (Matrix Transformation and Linear Transformation), the function L : R n → R n defined by L ( x ) = Ax , for x in R n , a linear transformation. A question of considerable importance in great many applied problems is the determination of vectors x , if they are any, such x and A x are parallel (see Figure 1). ● ● x x A x A x O O Figure 1: x is an eigenvector of A . A x is in same or opposite direction as x , if λ 6 = 0 EIGNEVALUES AND EIGENVECTORS 4 Such questions arise in all applications involving vibrations; they arise in aerodynamics, elasticity, nuclear physics, mechanics, chemical engineering, biology, differential equations, and others. In this section we shall formulate this problem precisely; we also define some pertinent terminology. In the next section we solve this problem for symmetric matrices and briefly discuss the situation in general case. Definition: Let A be an n × n matrix. The number λ is called an eigenvalue of A if there exists a nonzero vector x in R n such that A x = λ x . (1) Every nonzero vector x satisfying Equation (1) is called an eigenvector of A associated with the eigenvalue λ . The word eigenvalue is a hybrid one ( eigen in German means “proper"). Eigenvalues are also called proper values , characteristic values and latent values ; and eigenvectors are also called proper vectors , and so on, accordingly. Note that x = always satisfies Equation (1), but is not an eigenvector, since we insist that an eigenvector be a nonzero vector. Remark: In the preceding definition, the number λ can be real or complex and the vector x can have real or complex components. EIGNEVALUES AND EIGENVECTORS 6 Example 1 If A is the identity matrix I n , then the only eigenvalue is λ = 1 ; every nonzero vector in R n is an eigenvector of A associated with the eigenvalue λ = 1 : I n x = 1 x . Example 2 Let A = 1 2 1 2 . Then A 1 1 = 1 2 1 2 1 1 = 1 2 1 2 = 1 2 1 1 so that x 1 = 1 1 is an eigenvector of A associated with the eigenvalue λ 1 = 1 2 . Also A 1 1 = 1 2 1 2 1 1 =  1 2 1 2 = 1 2 1 1 so that x 2 = 1 1 is an eigenvector of A associated with the eigenvalue λ 2 = 1 2 ....
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This note was uploaded on 06/05/2011 for the course MATH 3333 taught by Professor Jeffwong during the Spring '11 term at CUHK.
 Spring '11
 JeffWong
 Linear Algebra, Algebra

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