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Rational Krylov-subspace methods are a predestined candidate in the reduction of very-large-scale linear models due to their moderate computational cost and memory requirements. However, in order to achieve good approximation results, state-of-the-art Krylov algorithms like IRKA iteratively search for a set of locally H2-optimal reduction parameters. This search requires the repeated reduction of...