Power iteration

In mathematics, the power iteration is an eigenvalue algorithm: given a matrix A, the algorithm will produce a number λ (the eigenvalue) and a nonzero vector v (the eigenvector), such that Av = λv. The algorithm is also known as the Von Mises iteration.

The power iteration is a very simple algorithm. It does not compute a matrix decomposition, and hence it can be used when A is a very large sparse matrix. However, it will find only one eigenvalue (the one with the greatest absolute value) and it may converge only slowly.

The method

The power iteration algorithm starts with a vector b₀, which may be an approximation to the dominant eigenvector or a random vector. The method is described by the recurrence relation

So, at every iteration, the vector b_k is multiplied by the matrix A and normalized.

If we assume A has an eigenvalue that is strictly greater in magnitude than its other eigenvalues and the starting vector has a nonzero component in the direction of an eigenvector associated with the dominant eigenvalue, then a subsequence converges to an eigenvector associated with the dominant eigenvalue.