Disguised and new Quasi-Newton methods for nonlinear eigenvalue problems
For researchers working on numerical methods for nonlinear eigenvalue problems, the paper provides a unifying framework and convergence analysis, but the results are incremental.
The paper develops quasi-Newton methods for nonlinear eigenvalue problems, showing that several existing methods can be interpreted as quasi-Newton methods and providing convergence theory. New algorithms are derived and analyzed.
In this paper we take a quasi-Newton approach to nonlinear eigenvalue problems (NEPs) of the type $M(λ)v=0$, where $M:\mathbb{C}\rightarrow\mathbb{C}^{n\times n}$ is a holomorphic function. We investigate which types of approximations of the Jacobian matrix lead to competitive algorithms, and provide convergence theory. The convergence analysis is based on theory for quasi-Newton methods and Keldysh's theorem for NEPs. We derive new algorithms and also show that several well-established methods for NEPs can be interpreted as quasi-Newton methods, and thereby provide insight to their convergence behavior. In particular, we establish quasi-Newton interpretations of Neumaier's residual inverse iteration and Ruhe's method of successive linear problems.