Local Multilevel Preconditioned Jacobi-Davidson Method for Elliptic Eigenvalue Problems on Adaptive Meshes

TL;DR

This paper introduces an efficient adaptive multilevel preconditioned Jacobi-Davidson method for elliptic eigenvalue problems, achieving optimal complexity and uniform convergence regardless of coefficient discontinuities.

Contribution

The work develops a local smoothing multilevel preconditioned Jacobi-Davidson method with proven optimal complexity and robustness for adaptive finite element eigenvalue problems.

Findings

Achieves optimal $O(N)$ computational complexity.

Demonstrates uniform convergence across mesh levels.

Numerical results confirm theoretical convergence and robustness.

Abstract

In this work, we propose an efficient adaptive multilevel preconditioned Jacobi-Davidson (PJD) method for eigenvalue problems with singularity. Our multilevel method utilizes a local smoothing strategy to solve the preconditioned Jacobi-Davidson algebraic systems arising from adaptive finite element methods (AFEM). As a result, the algorithm holds optimal computational complexity $O(N)$. The theoretical analysis reveals that our method has a uniform convergence rate with respect to mesh levels and degrees of freedom. Further, the convergence rate is not affected by highly discontinuous coefficients within the domain. Numerical results verify our theoretical findings.