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Lecture Notes 1: Matrix Algebra Part D: Similar Matrices and Diagonalization Peter J. Hammond minor revision 2020 September 26th University of Warwick, EC9A0 Maths for Economists Peter J. Hammond 1 of 76 Outline Eigenvalues and Eigenvectors Real Case The Complex Case Linear Independence of Eigenvectors Diagonalizing a General Matrix Similar Matrices Properties of Adjoint and Symmetric Matrices ASelf-Adjoint Matrix has only Real Eigenvalues Diagonalizing a Symmetric Matrix Orthogonal Matrices Orthogonal Projections Rayleigh Quotient The Spectral Theorem Quadratic Forms and Their Definiteness Quadratic Forms The Eigenvalue Test of Definiteness Sylvester’s Criterion for Definiteness University of Warwick, EC9A0 Maths for Economists Peter J. Hammond 2 of 76 Definitions in the Real Case Definition Consider any n ×n matrix A. The scalar λ ∈ R is an eigenvalue of A, just in case the equation Ax = λx has a non-zero solution. In this case the solution x ∈ Rn \ {0} is an eigenvector, and the pair (λ,x) is an eigenpair. The spectrum of the matrix A is the set SA of its eigenvalues. Let SR denote the subset of its real eigenvalues. A Let SC denote the subset of its complex eigenvalues, A C R which satisfies S =SA\S . A A University of Warwick, EC9A0 Maths for Economists Peter J. Hammond 3 of 76 Summary of Main Properties Wewill be demonstrating the following properties: 1. SR ⊆ S and #S ≤n A A A 2. The number #SC of complex eigenvalues is even, A C and the members of SA are complex conjugate pairs λ±µi. 3. SR = ∅ is possible in case n is even, but not if n is odd. A 4. In case A is symmetric, one has SC = ∅ and SR = SA. A A University of Warwick, EC9A0 Maths for Economists Peter J. Hammond 4 of 76
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