Abstract
The aim of the present paper is to give some new algebraic properties of the extended block and the extended global Arnoldi algorithms. These results are then applied on moment matching methods for model reductions in large-scale dynamical systems to get low-order models that approximate the original models by matching moments and Markov parameters at the same time. Some numerical examples are given to show the effectiveness of the methods on some benchmark tests.
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