Efficient Balance-and-Truncate Model Reduction for
Large Scale Systems
Efficient Balance-and-Truncate Model Reduction for
Large Scale Systems
V. Balakrishnan, Q. Su, and C-K. Koh
Technical Report TR-ECE 00-15, School of Electrical and Computer Engineering,
Purdue University, November 2000
Abstract: We present efficient implementations of the
balance-and-truncate model reduction technique for large-scale
systems. The key observation that distinguishes our approach is that
Krylov subspace methods (Arnoldi and Lanczos) directly yield
approximate low-rank square roots of the system Gramians; the
balancing transformation can then be constructed from these square
roots, obviating the need for solving any Lyapunov equations. In
addition, the order of the reduced model is not fixed a priori as with
some existing methods, but is determined from the problem data.
Numerical simulations show that our approach performs very well over a
range of examples, and offers considerable savings in practice.
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