The inexact residual iteration method for quadratic eigenvalue problem and the analysis of convergence
For researchers solving large-scale quadratic eigenvalue problems, this work provides a theoretically grounded inexact method with convergence guarantees, though it is an incremental improvement over existing residual iteration approaches.
The paper establishes convergence criteria for the residual iteration method for quadratic eigenvalue problems and proposes an inexact version that uses iterative solvers for large-scale problems. Numerical experiments confirm the analysis and show the method's effectiveness.
In this paper, we first establish the convergence criteria of the residual iteration method for solving quadratic eigenvalue problem- s. We analyze the impact of shift point and the subspace expansion on the convergence of this method. In the process of expanding subspace, this method needs to solve a linear system at every step. For large scale problems in which the equations cannot be solved directly, we propose an inner and outer iteration version of the residual iteration method. The new method uses the iterative method to solve the equations and uses the approximate solution to expand the subspace. We analyze the relationship between inner and outer iterations and provide a quantita- tive criterion for the inner iteration which can ensure the convergence of the outer iteration. Finally, our numerical experiments provide proof of our analysis and demonstrate the effectiveness of the inexact residual iteration method.