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Lecture 25 Quadratic Forms [???E????] Lecture 25: 7.2 Orthogonal Diagonalization Wei-Ta Chu 2011/12/21 Spectral Decomposition If A is a symmetric matrix that is orthogonally diagonalized by λ λ λ P=[ u1 u2 … un], and if 1, 2, …, n are the eigenvalues of A corresponding to the unit eigenvectors u1, u2, …, un, then we know that D=PTAP , where D is a diagonal matrix with the eigenvalues in the diagonal positions. 2 Spectral Decomposition Multiplying out, we obtain the formula which is called a spectral decomposition of A. Each term of the spectral decomposition of A has the form where u is a unit eigenvector of A in column form, and λ is an eigenvalue of A corresponding to u. It can be proved that uu T is the standard matrix for the orthogonal projection of Rn on the subspace spanned by the vector u. 3 Spectral Decomposition The spectral decomposition of A tells that the image of a vector x under multiplication by a symmetric matrix A can be obtained by projecting x orthogonally on the lines determined by the eigenvectors of A, then scaling those projections by the eigenvalues, and then adding the scaled projections. 4 Example: Eigenface Face database u1 u2 u3 … Mean face Eigenvectors 5 Example of Face Reconstruction x Ax = -2181 +627 +389 + … Reconstruction procedure 6 Example λ λ The matrix has eigenvalues 1=-3 and 2=2 with corresponding eigenvectors x1=(1,-2) and x2=(2,1) Normalizing these basis vectors yields A spectral docomposition of A is The standard matrices for the orthogonal projections onto the eigenspaces 7 corresponding to λ1=-3 and λ2=2 Example The image of the vector x=(1,1) These provide two different ways of viewing the image of the vector (1,1) under multiplication by A 8 Lecture 25: 7.3 Quadratic Forms Wei-Ta Chu 2011/12/21 Quadratic Form ( 二次式) Up to now, we have been interested in linear equations It’s a function of n variables, called a linear form. We will be concerned with quadratic forms , which are functions of the form For example: cross-product terms 10 Quadratic Form Written in matrix form They are both of the form xTAx, where x is the column vector of variables, and A is symmetric matrix whose diagonal entries are the coefficients of the squared terms and whose entries off the main diagonal are half the coefficients of the cross-product terms. 11 Example 12 Symmetric Matrix Symmetric matrices are useful, but not essential, for representing quadratic forms. For example, the quadratic form 2 x2+6 xy -7y2 can be written as where the coefficient 6 of the cross-product term has been split as 5+1 rather than 3+3, as in the symmetric representation. 13 Symmetric Matrix However, symmetric matrices are usually more convenient to work with, so it will always be understood that A is symmetric when we write a quadratic form as xTAx, even if not stated explicitly. When convenient, we can use Formula (7) of Section 4.1 to express a quadratic form xTAx in terms of the Euclidean inner product as 14 Problems Problem 1: if xTAx is a quadratic form on R2 or R3, what kind of curve or surface is represented by the equation xTAx=k? Problem 2: if xTAx is a quadratic form on Rn, what conditions must A satisfy for xTAx to have positive values for x ≠ 0? Problem 3: if xTAx is a quadratic form on Rn, what are its maximum and minimum values if x is constrained to satisfy || x|| = 1? 15 Change of Variable Simplify the quadratic form xTAx by making a substitution x=Py. That expresses the variable x1, x2, …, xn in terms of new variables y1, y2, …, yn. If P is invertible, we call this change of variable . If P is orthogonal, then we call this orthogonal change of variable . We obtain: xTAx=( Py)TA(Py)= yTPTAP y=yT(PTAP )y Since the matrix B=PTAP is symmetric, the effect of the change of variable is to produce a new quadratic form yTBy. 16 Change of Variable If we choose P to orthogonally diagonalize A, then the new quadratic form will be yTDy, where D is a diagonal matrix with the eigenvalues of A on the main diagonal. 17 Theorem 7.3.1 The Principal Axes Theorem If A is a symmetric n by n matrix, then there is an orthogonal change of variable that transforms the quadratic form xTAx into a quadratic form yTDy with no cross product terms. Specifically, if P orthogonally diagonalize A, then making the change of variable x=Py in the quadratic form xTAx yields the quadratic form T T λ 2 λ 2 λ 2 x Ax=y Dy= 1y1 + 2y2 +…+ nyn λ λ λ in which 1, 2,…, n are the eigenvalues of A corresponding to the eigenvectors that form the successive columns of P. 18 Example Find an orthogonal change of variable of the quadratic form 2 2 Q=x1 -x3 -4x1x2+4 x2x3. The characteristic equation of the matrix A is The eigenvalues are 0, -3, 3. The orthonormal bases for the three eigenspace are 19 Example A substitution x=Py that eliminates the cross product terms is This produces the new quadratic form 20 Positive Definite ( 正定) Definition: A quadratic form xTAx is called positive definite if xTAx > 0 for all x ≠ 0, negative definite if xTAx < 0 for x ≠ 0 , indefinite if xTAx has both positive and negative values It’s called positive semidefinite if xTAx ≧ 0 for all x ≠ 0, and negative semidefinite if xTAx ≦ 0 for all x ≠ 0 21 Theorem 7.3.2 If A is a symmetric matrix A, then T x Ax is positive definite if and only if all eigenvalues of A are positive. T x Ax is negative definite if and only if all eigenvalues of A are negative. T x Ax is indefinite if and only if A has at least one positive eigenvalue and at least one negative eigenvalue. 22 Example We showed that the symmetric matrix has eigenvalues and . Since these are positive, the matrix A is positive definite, and for all x ≠ 0, 23 Identifying Positive Definite Matrices Positive definite matrices are the most important symmetric matrices in applications. A method to determine whether a symmetric matrix is positive definite without finding the eigenvalues. The kth principal submatrix : First principal submatrix Third principal submatrix Second principal submatrix Fourth principal submatrix 24 Theorem 7.3.4 A symmetric matrix A is positive definite if and only if the determinant of every principal submatrix is positive. Example: We are guaranteed that all eigenvalues of A are positive and xTAx > 0 for x ≠ 0. 25 Exercises Sec. 7.1: 2, 6, 17, 22(True-False) Sec. 7.2: 5, 16(c), 18(a), 22(True-False) Sec. 7.3: 6, 12, 21, 25(b), 36(True-False) 26 Lecture 25: 8.1 General Linear Transformations Wei-Ta Chu 2011/12/21 Definitions and Terminology n m A matrix transformation TA: R →R is a mapping of the form TA(x) = Ax, in which A is an m by n matrix. Matrix transformations are precisely the linear transformations from Rn to Rm. The transformations with linearity properties T(u+v) = T(u) + T(v) and T(ku) = kT (u) 28 Definitions and Terminology If T: V → W is a function from a vector space V to a vector space W, then T is called a linear transformation from V to W if the following two properties hold for all vectors u and v in V and all scalars k: (1) T(ku) = kT (u) [Homogeneity property] (2) T(u+v) = T(u) + T(v) [Additivity property] In the special case where V = W, the linear transformation is called a linear operator on the vector space V. 29 Definitions and Terminology These properties can be used in combination T(k1v1 + k2v2) = k1T(v1) + k2T(v2) More generally, T(k1v1 + k2v2+ …+ krvr) = k1T(v1) + k2T(v2) + … + krT(vr) Theorem 8.1.1: If T: V → W is a linear transformation, then (a) T(0) = 0 (b) T(u-v) = T(u) – T(v) for all u and v in V 30 Example n m A matrix transformation TA: R → R is also a liner transformation in this more general sense with V = Rn and W = Rm The zero transformation: The mapping T: V → W such that T(v) = 0 for every v in V is a linear transformation called the zero transformation . T is linear: T(u+v) = 0, T(u) = 0, T(v) = 0, and T(ku) = 0 Therefore, T(u+v) = T(u) + T(v), and T(ku) = kT (u) 31 Example The identity operator: The mapping I: V → V defined by I(v) = v is called the identity operator on V. The mapping T: V → V given by T(x) = kx is a linear operator on V, for if c is any scalar and if u and v are any vectors in V, then T(cu) = k(cu) = c(ku) = cT (u) T(u+v) = k(u+v) = ku + kv = T(u) + T(v) If 0 < k < 1, then T is called the contraction of V with factor k, and if k > 1, it is called the dilation of V with factor k. 32 Example n Let p = p(x) = c0 + c1x + … + cnx be a polynomial in Pn, and define the transformation T: Pn → Pn+1 by 2 n+1 T(p) = T(p(x)) = xp (x) = c0x + c1x + … + cnx This transformation is linear because for any scalar k and any polynomial p1 and p2 in Pn we have T(kp) = T(kp (x)) = x(kp (x)) = k(xp (x)) = kT (p) T(p1 + p2) = T(p1(x) + p2(x)) = x(p1(x) + p2(x)) = xp 1(x) + xp 2(x) = T(p1) + T(p2) 33 Example Let V be an inner product space, let v0 be any fixed vector in V, and let T: V → R be the transformation T(x) = 〈x,v0〉 that maps a vector x into its inner product with v0.
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