Stability Analysis of Bilinear Iterative Rational Krylov Algorithm
For researchers using BIRKA for model reduction, this work provides theoretical justification for using iterative solvers instead of expensive direct methods, potentially reducing computational cost.
The paper proves that the Bilinear Iterative Rational Krylov Algorithm (BIRKA) for model reduction of bilinear dynamical systems is stable with respect to errors from inexact linear solves (e.g., iterative methods), and analyzes the accuracy of the resulting reduced model, supported by numerical experiments.
Models coming from different physical applications are very large in size. Simulation with such systems is expensive so one usually obtains a reduced model (by model reduction) that replicates the input-output behaviour of the original full model. A recently proposed algorithm for model reduction of bilinear dynamical systems, Bilinear Iterative Rational Krylov Algorithm (BIRKA), does so in a locally optimal way. This algorithm requires solving very large linear systems of equations. Usually these systems are solved by direct methods (e.g., LU), which are very expensive. A better choice is iterative methods (e.g., Krylov). However, iterative methods introduce errors in linear solves because they are not exact. They solve the given linear system up to a certain tolerance. We prove that under some mild assumptions BIRKA is stable with respect to the error introduced by the inexact linear solves. We also analyze the accuracy of the reduced system obtained from using these inexact solves and support all our results by numerical experiments.