Concept
In linear algebra, eigendecomposition is the factorization of a matrix into a canonical form, whereby the matrix is represented in terms of its eigenvalues and eigenvectors. Only diagonalizable matrices can be factorized in this way. When the matrix being factorized is a normal or real symmetric matrix, the decomposition is called "spectral decomposition", derived from the spectral theorem. Eigenvalue, eigenvector and eigenspace A (nonzero) vector v of dimension N is an eigenvector of a square N × N matrix A if it satisfies a linear equation of the form for some scalar λ. Then λ is called the eigenvalue corresponding to v. Geometrically speaking, the eigenvectors of A are the vectors that A merely elongates or shrinks, and the amount that they elongate/shrink by is the eigenvalue. The above equation is called the eigenvalue equation or the eigenvalue problem. This yields an equation for the eigenvalues We call p(λ) the characteristic polynomial, and the equation, called the characteristic equation, is an Nth order polynomial equation in the unknown λ. This equation will have Nλ distinct solutions, where 1 ≤ Nλ ≤ N. The set of solutions, that is, the eigenvalues, is called the spectrum of A. If the field of scalars is algebraically closed, then we can factor p as The integer ni is termed the algebraic multiplicity of eigenvalue λi. The algebraic multiplicities sum to N: For each eigenvalue λi, we have a specific eigenvalue equation There will be 1 ≤ mi ≤ ni linearly independent solutions to each eigenvalue equation. The linear combinations of the mi solutions (except the one which gives the zero vector) are the eigenvectors associated with the eigenvalue λi. The integer mi is termed the geometric multiplicity of λi. It is important to keep in mind that the algebraic multiplicity ni and geometric multiplicity mi may or may not be equal, but we always have mi ≤ ni. The simplest case is of course when mi = ni = 1.