Lecture_12

# Lecture_12 - Lecture Note 13 Nov 28 Dec 1 2006 Dr Jeff...

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Unformatted text preview: Lecture Note 13: Nov 28 - Dec 1, 2006 Dr. Jeff Chak-Fu WONG Department of Mathematics Chinese University of Hong Kong [email protected] MAT 2310 Linear Algebra and Its Applications Fall, 2006 Produced by Jeff Chak-Fu WONG 1 S INGULAR V ALUE D ECOMPOSITION SINGULAR VALUE DECOMPOSITION 2 The diagonalization theorem Lecture Note 11-1 and 11-2 play a part in many interesting applications. Unfortunately, as we know, not all matrices can be factored as A = PDP- 1 with D diagonal. However, a factorization A = QDP- 1 is possible for any m × n matrix A ! A special factorization of this type, the singular value decomposition , is one of the most useful matrix factorizations in applied linear algebra. SINGULAR VALUE DECOMPOSITION 3 Our aim is to factor any m × n matrix A in the form A = U Σ V T where U is m × m , V is n × n , and they are both orthogonal. Also Σ is an m × n matrix with a diagonal upper left block of positive entries of decreasing magnitude and the remaining entries 0. Thus Σ =       D . . . ... ... ... . . .       , where D =      σ 1 ... . . . . . . . . . ... σ r      and σ 1 ≥ σ 2 ≥ ... ≥ σ r > , r ≤ m,n A m × n = U m × m Σ m × n V T n × n SINGULAR VALUE DECOMPOSITION 4 The singular value decomposition is based on the following property of the ordinary diagonalization that can be imitated for rectangular matrices: The absolute values of the eigenvalue of a symmetric matrix A measure the amounts that A stretches or shrinks certain vectors (the eigenvectors) . If A x = λ x and k x k = 1 , then k A x k = k λ x k = | λ |k x k = | λ | (1) If λ i is the eigenvalue with the greatest magnitude, then a corresponding unit eigenvector v 1 identifies a direction in which the stretching effect of A is greatest. That is, the length of A x is maximized when x = v 1 and k A v 1 k = | λ 1 | , by (1). This description of v 1 and | λ 1 | has an analogue for rectangular matrices that will lead to the singular value decomposition. SINGULAR VALUE DECOMPOSITION 5 T HE S INGULAR V ALUES OF AN m × n M ATRIX THE SINGULAR VALUES OF AN m × n MATRIX 6 Let A be an m × n matrix. Then A T A is symmetric and can be orthogonally diagonalized . Let { v 1 ,..., v n } be an orthonormal basis for R n consisting of eigenvectors of A T A . Here, V is simply as V = h v 1 v 2 ··· v n i n × n Let λ 1 ,...,λ n be the associated eigenvalues of A T A . Then for 1 ≤ i ≤ n , ≤ k A v i k 2 = ( A v i ) T A v i = v T i A T A v i = v T i ( λ i v i ) Since v i is a eigenvector of A T A = λ i k v i k 2 = λ i Since v i is a unit vector (2) So the eigenvalue of A T A are all nonnegative (so A T A is positive semi definite). By renumbering, if necessary, we may assume that the eigenvalues are arranged so that λ 1 ≥ λ 2 ≥ ... ≥ λ n ≥ THE SINGULAR VALUES OF AN m × n MATRIX 7 The singular values of A are the square roots of the eigenvalues of A T A , denoted by σ 1...
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Lecture_12 - Lecture Note 13 Nov 28 Dec 1 2006 Dr Jeff...

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