Passivity-Preserving Model Reduction Via A Computationally Efficient
Project-And-Balance Scheme
Passivity-Preserving Model Reduction Via A Computationally Efficient
Project-And-Balance Scheme
N. Wong, V. Balakrishnan, and C.-K. Koh
In Proc. \ 2004
Design Automation Conference (DAC) ,
San Diego, California, June 2004
Abstract:
This paper presents an efficient two-stage project-and-balance scheme
for passivity-preserving model order reduction. Orthogonal dominant
eigenspace projection is implemented by integrating the Smith method
and Krylov subspace iteration. It is followed by stochastic balanced
truncation wherein a novel method, based on the complete separation of
stable and unstable invariant subspaces of a Hamiltonian matrix, is
used for solving two dual algebraic Riccati equations at the cost of
essentially one. A fast-converging quadruple-shift bulge-chasing SR
algorithm is also introduced for this purpose. Numerical examples
confirm the quality of the reduced-order models over those from
conventional schemes.
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