摘要：AbstractIn this contribution, we present a model order reduction algorithm for linear systems with multiple inputs and multiple outputs that aims at finding theglobaloptimal reduced model of prescribed ordern, with respect to theH2norm. Our approach is based onglobalized local optimization,which requires a global sampling of the search space and subsequent localH2optimization. The increased cost resulting from repeatedH2optimization will be mitigated by exploiting theModel Functionframework forH2-optimal model reduction, making the optimization cost negligible compared to the cost of reduction. Numerical investigations motivate the need for globalized approaches inH2-optimal reduction and demonstrate how our method is capable of finding global optima, at a far lower cost than running conventionalH2-optimal reduction for different initial samples.
关键词：KeywordsMinisymposium on Model Reductionmodel reductionmodel approximationlarge-scale systemsMIMOglobal optimization
