A quasi-convex optimization approach to parameterized model order reduction

Abstract—In this paper, an optimization-based model order reduction (MOR) framework is proposed. The method involves setting up a quasi-convex program that solves a relaxation of the optimal H ∞ norm MOR problem. The method can generate guar-anteed stable and passive reduced models and is very flexible in imposing additional constraints such as exact matching of specific frequency response samples. The proposed optimization-based approach is also extended to solve the parameterized model-reduction problem (PMOR). The proposed method is compared to existing moment matching and optimization-based MOR methods in several examples. PMOR models for large RF inductors over substrate and power-distribution grid are also constructed. Index Terms—Parameterized model order reduction (PMOR), quasi-convex optimization, RF inductor. I