An approximative solution of the generalized eigenvalue problem
We will discuss Kellogg's iterations in eigenvalue problems for normal operators. A certain generalisation of the convergence theorem is shown.
Kellogg's iterations in the eigenvalue problem are discussed with respect to the boundary spectrum of a linear normal operator.
We provide a short overview of algorithms useful for computing of stationary probability vectors of stochastic matrix. Some care is devoted to the problem of computing of all extremal stationary probability vectors for the reducible stochastic matrices. We present some modifications of standard Iterative Aggregation/Disaggregation algorithm.
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