|
Size: 1961
Comment: Diagonalizability
|
Size: 1348
Comment: Simplifying matrix page names
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 6: | Line 6: |
---- == Symmetry == A '''symmetric matrix''' is equal to its [[LinearAlgebra/MatrixTransposition|transpose]]. {{{ julia> A = [1 2; 2 1] 2×2 Matrix{Int64}: 1 2 2 1 julia> A == A' true }}} ---- == Invertability == A matrix is '''invertible''' and '''non-singular''' if the [[LinearAlgebra/Determinants|determinant]] is non-zero. |
|
| Line 43: | Line 17: |
| ---- == Orthonormality == A [[LinearAlgebra/Orthogonality#Matrices|matrix with orthonormal columns]] has several important properties. A matrix '''''A''''' can be [[LinearAlgebra/Orthonormalization|orthonormalized]] into '''''Q'''''. === Orthogonality === An '''orthogonal matrix''' is a ''square'' matrix with orthonormal columns. |
Only a square matrix can be idempotent. |
| Line 61: | Line 23: |
| == Diagonalizability == | == Positive Definite == |
| Line 63: | Line 25: |
| A '''diagonalizable matrix''' is a ''square'' matrix that can be factored as '''''A''' = '''SΛS'''^-1^''. '''''S''''' will be the [[LinearAlgebra/EigenvaluesAndEigenvectors|eigenvector]] matrix and '''''Λ''''' will be a zero matrix with [[LinearAlgebra/EigenvaluesAndEigenvectors|eigenvalues]] in the diagonal. | A '''positive definite matrix''' is a symmetric matrix where all [[LinearAlgebra/EigenvaluesAndEigenvectors|eigenvalues]] are positive. Following from the properties of all symmetric matrices, all pivots are also positive. Necessarily the [[LinearAlgebra/Determinants|determinant]] is also positive, and all subdeterminants are also positive. |
| Line 65: | Line 27: |
| A square matrix that is not diagonalizable is called '''defective'''. | === Positive Semi-definite === A slight modification of the above requirement: 0 is also allowable. |
Matrix Properties
Matrices can be categorized by whether or not they feature certain properties.
Idempotency
An idempotent matrix can be multiplied by some matrix A any number of times and the first product will continue to be returned. In other words, A2 = A.
For example, the projection matrix P is characterized as H(HTH)-1HT. If this were squared to H(HTH)-1HTH(HTH)-1HT, then per the core principle of inversion (i.e., AA-1 = I), half of the terms would cancel out. P2 = P.
Only a square matrix can be idempotent.
Positive Definite
A positive definite matrix is a symmetric matrix where all eigenvalues are positive. Following from the properties of all symmetric matrices, all pivots are also positive. Necessarily the determinant is also positive, and all subdeterminants are also positive.
Positive Semi-definite
A slight modification of the above requirement: 0 is also allowable.
