Domain-Alternated Optimization for Passive Macromodeling

Passivity enforcement is an important issue for macromodeling for passive systems from measured or simulated data. Existing passivity enforcement techniques based on iteratively fixing the passivity either suffer from convergence issue or lack optimality that will sometimes lead to unacceptable error. In addition to the traditional two-stage (fitting plus enforcement) schemes, we propose a postenforcement optimization, which takes a passive yet not necessarily accurate model as the starting point, and performs local search to find the local optimum. A new technique, called domain-alternated optimization is proposed to eliminate passivity constraints while still guarantees strict passivity during the optimization. Experiments show that taking the models generated from existing enforcement methods, the proposed method can provide significant improvement on accuracy. The proposed method is efficient and can deal with problems up to a few tens of thousands of variables.