Wednesday, 25 February 2009

Computing Similarity Transformation


Eigen Decomposition

The matrix decomposition of a square matrix A into so-called eigenvalues and eigenvectors is an extremely important one. This decomposition generally goes under the name "matrix diagonalization." However, this moniker is less than optimal, since the process being described is really the decomposition of a matrix into a product of three other matrices, only one of which is diagonal, and also because all other standard types of matrix decomposition use the term "decomposition" in their names, e.g., Cholesky decomposition, Hessenberg decomposition, and so on. As a result, the decomposition of a matrix into matrices composed of its eigenvectors and eigenvalues is called eigen decomposition in this work.


giving the amazing decomposition of A into a similarity transformation involving P and D,

 A=PDP^(-1). (12)

The fact that this decomposition is always possible for a square matrix A as long as P is a square matrix is known in this work as the eigen decomposition theorem.

Furthermore, squaring both sides of equation (12) gives




By induction, it follows that for general positive integer powers,

 A^n=PD^nP^(-1). (16)

The inverse of A is



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