We introduce an interpolation framework for H∞ model reduction founded on ideas originating in optimal-H2 interpolatory model reduction, realization theory, and complex Chebyshev approximation. By employing a Loewner “data-driven” framework within each optimization cycle, large-scale H∞ norm calculations can be completely avoided. Thus, we are able to formulate a method that remains effective i...

A general framework for structure-preserving model reduction by Krylov subspace projection methods is developed. The goal is to preserve any substructures of importance in the matrices L, G, C, B that define the model prescribed by transfer function H(s) = L∗(G+sC)−1B. Many existing structure-preserving model-order reduction methods for linear and second-order dynamical systems can be derived u...