Automoderated users, edit
3,552
edits
Changes
Jump to navigation
Jump to search
Line 1:
Line 1: − {{merge|eigenvector}}+ − An '''eigenvalue''' of a square [[matrix]] <math>A</math> is a scalar <math>\lambda</math> such that − − :<math>A\boldsymbol{x}=\lambda\boldsymbol{x}</math> − − for some non-zero vector <math>\boldsymbol{x}\in\mathbb{R}^n</math> known as a [[eigenvector]]. The eigenvalues are the zeroes of a matrix's [[characteristic polynomial]], the degree of the corresponding root is called the '''algebraic multiplicity''' of the eigenvalue. − − If the characteristic polynomial splits into linear factors, then he product of all the eigenvalues of a matrix counted with their algebraic multiplicities equals the value of the matrix's determinant. Since a matrix is invertible if and only if the determinant is non-zero, it is invertible if and only if zero is not an eigenvalue. − − The [[span]] of all the eigenvectors corresponding to a fixed eigenvalue <math>\lambda</math> is called the [[eigenspace]] <math>E_\lambda</math> of <math>A</math>. The dimension of this space is called the '''geometric multiplicity''' of the eigenvalue. − − [[category:Linear algebra]]
Redirected page to Eigenvectors and Eigenvalues
#REDIRECT[[Eigenvectors and Eigenvalues]]