We don't need to check all the leading principal minors because once det M is nonzero, we can immediately deduce that M has no zero eigenvalues, and since it is also given that M is neither positive definite nor negative definite, then M can only be indefinite. A negative definite matrix is a Hermitian matrix all of whose eigenvalues are negative. A real matrix is symmetric positive definite if it is symmetric (is equal to its transpose, ) and. I Example: The eigenvalues are 2 and 1. The The quadratic form of A is xTAx. The rules are: (a) If and only if all leading principal minors of the matrix are positive, then the matrix is positive definite. The quadratic form of a symmetric matrix is a quadratic func-tion. So r 1 =1 and r 2 = t2. definite or negative definite (note the emphasis on the matrix being symmetric - the method will not work in quite this form if it is not symmetric). To say about positive (negative) (semi-) definite, you need to find eigenvalues of A. Satisfying these inequalities is not sufficient for positive definiteness. NEGATIVE DEFINITE QUADRATIC FORMS The conditions for the quadratic form to be negative definite are similar, all the eigenvalues must be negative. A matrix A is positive definite fand only fit can be written as A = RTRfor some possibly rectangular matrix R with independent columns. The matrix is said to be positive definite, if ; positive semi-definite, if ; negative definite, if ; negative semi-definite, if ; For example, consider the covariance matrix of a random vector Example-For what numbers b is the following matrix positive semidef mite? I Example, for 3 × 3 matrix, there are three leading principal minors: | a 11 |, a 11 a 12 a 21 a 22, a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33 Xiaoling Mei Lecture 8: Quadratic Forms and Definite Matrices 12 / 40 Note that we say a matrix is positive semidefinite if all of its eigenvalues are non-negative. By making particular choices of in this definition we can derive the inequalities. For example, the quadratic form of A = " a b b c # is xTAx = h x 1 x 2 i " a b b c #" x 1 x 2 # = ax2 1 +2bx 1x 2 +cx 2 2 Chen P Positive Definite Matrix Positive/Negative (semi)-definite matrices. So r 1 = 3 and r 2 = 32. 4 TEST FOR POSITIVE AND NEGATIVE DEFINITENESS 3. Since e 2t decays and e t grows, we say the root r 1 = 3 is the dominantpart of the solution. Associated with a given symmetric matrix , we can construct a quadratic form , where is an any non-zero vector. For example, the matrix. Since e 2t decays faster than e , we say the root r 1 =1 is the dominantpart of the solution. For example, the matrix = [] has positive eigenvalues yet is not positive definite; in particular a negative value of is obtained with the choice = [−] (which is the eigenvector associated with the negative eigenvalue of the symmetric part of ). 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