How to make the Perron eigenvector simple

Multiple Perron eigenvectors of non-negative matrices occur in applications, where they often become a source of trouble. A usual way to avoid it and to make the Perron eigenvector simple is a regularization of matrix: an initial non-negative matrix A is replaced by A + ε M , where M is a strictly positive matrix and ε > 0 is small. However, this operation is numerically unstable and may lead to a significant increase of the Perron eigenvalue, especially in high dimensions. We define a selected Perron eigenvector of A as the limit of normalized Perron eigenvectors of the regularizations A + ε M as ε → 0 . It is shown that if the matrix M is rank-one, then the limit eigenvector can be found by an explicit formula and, moreover, is efficiently computed by the power method. The role of the rank-one condition is explained.