Local Multilevel Preconditioned Jacobi-Davidson Method for Elliptic Eigenvalue Problems on Adaptive Meshes
This work provides an efficient solver for eigenvalue problems in adaptive finite element methods, addressing the challenge of singularities and discontinuous coefficients.
The authors propose a local multilevel preconditioned Jacobi-Davidson method for elliptic eigenvalue problems on adaptive meshes, achieving optimal O(N) computational complexity and uniform convergence independent of mesh levels and discontinuous coefficients.
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.