Combining Krylov subspace methods and identification-based methods for model order reduction
نویسندگان
چکیده
Many different techniques to reduce the dimensions of a model have been proposed in the near past. Krylov subspace methods are relatively cheap, but generate non-optimal models. In this paper a combination of Krylov subspace methods and Orthonormal Vector Fitting is proposed. In that way an optimal model for a large model can be generated. In the first step, a Krylov subspace method reduces the large model to a model of medium size, then an optimal model is derived with Orthonormal Vector Fitting as a second step.
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