Algorithms for model reduction of large dynamical systems

摘要: Three algorithms for the model reduction of large-scale, continuous-time, time-invariant, linear, dynamical systems with a sparse or structured transition matrix and small number inputs outputs are described. They rely on low rank approximations to controllability observability Gramians, which can efficiently be computed by ADI based iterative methods. The first two methods closely related well-known square root method Schur method, balanced truncation techniques. third is heuristic, balancing-free technique. performance studied in numerical experiments.