Preconditioned eigensolvers for large-scale nonlinear Hermitian eigenproblems with variational characterizations. II. Interior eigenvalues
This work addresses the need for efficient eigensolvers for interior eigenvalues in nonlinear Hermitian problems, which are common in various applications.
The paper proposes a Preconditioned Locally Minimal Residual (PLMR) method for computing interior eigenvalues of large-scale nonlinear Hermitian eigenproblems with variational characterizations. Numerical experiments show rapid and robust convergence, dramatically outperforming standard preconditioned conjugate gradient methods.
We consider the solution of large-scale nonlinear algebraic Hermitian eigenproblems of the form $T(λ)v=0$ that admit a variational characterization of eigenvalues. These problems arise in a variety of applications and are generalizations of linear Hermitian eigenproblems $Av\!=\!λBv$. In this paper, we propose a Preconditioned Locally Minimal Residual (PLMR) method for efficiently computing interior eigenvalues of problems of this type. We discuss the development of search subspaces, preconditioning, and eigenpair extraction procedure based on the refined Rayleigh-Ritz projection. Extension to the block methods is presented, and a moving-window style soft deflation is described. Numerical experiments demonstrate that PLMR methods provide a rapid and robust convergence towards interior eigenvalues. The approach is also shown to be efficient and reliable for computing a large number of extreme eigenvalues, dramatically outperforming standard preconditioned conjugate gradient methods.