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+ εM, where M is a strictly positive matrix and ε&gt; 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&nbsp;A as the limit of normalized Perron eigenvectors of the regularizations A+ εM 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.

### How to make the Perron eigenvector simple

#### Abstract

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+ εM, 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+ εM 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: `http://hdl.handle.net/11697/159779`
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