An Approach for Globalized H2-Optimal Model Reduction⁎. Issue 2 (2018) - Castagnotto, Alessandro; Hu, Siyang; Lohmann, Boris
Abstract: In this contribution, we present a model order reduction algorithm for linear systems with multiple inputs and multiple outputs that aims at finding the global optimal reduced model of prescribed order n, with respect to the H 2 norm. Our approach is based on globalized local optimization, which requires a global sampling of the search space and subsequent local H 2 optimization. The increased cost resulting from repeated H 2 optimization will be mitigated by exploiting the Model Function framework for H 2 -optimal model reduction, making the optimization cost negligible compared to the cost of reduction. Numerical investigations motivate the need for globalized approaches in H 2 -optimal reduction and demonstrate how our method is capable of finding global optima, at a far lower cost than running conventional H 2 -optimal reduction for different initial samples.