Residual minimizing shift parameters for the low-rank ADI iteration
This work addresses the difficult task of selecting shift parameters for the LR-ADI iteration, particularly for nonsymmetric Lyapunov equations, offering a more effective dynamic approach.
The paper proposes self-generating shift parameters for the low-rank ADI iteration that minimize the Lyapunov residual norm using compressed objective functions, outperforming existing precomputed and dynamic shift selection techniques in numerical experiments.
The low-rank alternating directions implicit (LR-ADI) iteration is a frequently employed method for efficiently computing low-rank approximate solutions of large-scale Lyapunov equations. In order to achieve a rapid error reduction, the iteration requires shift parameters whose selection and generation is often a difficult task, especially for nonsymmetric coefficients in the Lyapunov equation. This article represents a follow up of Benner et al. [ETNA, 43 (2014-2015), pp. 142-162] and investigates self-generating shift parameters based on a minimization principle for the Lyapunov residual norm. Since the involved objective functions are too expensive to evaluate and, hence, intractable, compressed objective functions are introduced which are efficiently constructed from the available data generated by the LR-ADI iteration. Several numerical experiments indicate that these residual minimizing shifts using approximated objective functions outperform existing precomputed and dynamic shift parameter selection techniques, although their generation is more involved.