“…where \varepsi > 0 is a properly chosen ``absorption"" parameter, and \mu > 0 is a suitably chosen constant; see, e.g., [12,13,16,18,20,37] in the context of multigrid solvers and [18,28,39] involving domain decomposition methods. The behavior of such preconditioners and choices of shifting parameters have also been analyzed [15,16,19]; typically \varepsi = O(\kappa 2 ) or \varepsi = O(\kappa ) are used.…”
Section: Applications To Noncoercive Boundary Value Problemsmentioning
confidence: 99%
“…Then section 5 provides some applications in H 1 spaces. Here detailed attention is paid to interior Helmholtz equations and shifted Laplace preconditioners, to which a lot of recent research has been devoted (e.g., [13,25,20,37]), however only focused on the aspects of linear convergence. We also indicate other applications and further directions.…”
“…where \varepsi > 0 is a properly chosen ``absorption"" parameter, and \mu > 0 is a suitably chosen constant; see, e.g., [12,13,16,18,20,37] in the context of multigrid solvers and [18,28,39] involving domain decomposition methods. The behavior of such preconditioners and choices of shifting parameters have also been analyzed [15,16,19]; typically \varepsi = O(\kappa 2 ) or \varepsi = O(\kappa ) are used.…”
Section: Applications To Noncoercive Boundary Value Problemsmentioning
confidence: 99%
“…Then section 5 provides some applications in H 1 spaces. Here detailed attention is paid to interior Helmholtz equations and shifted Laplace preconditioners, to which a lot of recent research has been devoted (e.g., [13,25,20,37]), however only focused on the aspects of linear convergence. We also indicate other applications and further directions.…”
“…For instance, in the work of Erlangga et al, M −1 represents an action of multigrid preconditioner for the Helmholtz matrix, which acts on the shifted Laplace (or modified Helmholtz) matrix. Theoretical results for the MK method (two‐level) applied to the Helmholtz equation can be found in the works of García Ramos et al and Erlangga et al Furthermore, the inclusion of such a matrix is done in MK via a modification of the original matrix A to , , or , where M 1 M 2 = M . For the Galerkin‐type approach, this modification leads to a coarse‐grid structure, which is different from that in standard multigrid/level methods.…”
We discuss the convergence of a two-level version of the multilevel Krylov method for solving linear systems of equations with symmetric positive semidefinite matrix of coefficients. The analysis is based on the convergence result of Brown and Walker for the Generalized Minimal Residual method (GMRES), with the left-and right-preconditioning implementation of the method. Numerical results based on diffusion problems are presented to show the convergence.
We investigate the Helmholtz equation with suitable boundary conditions and uncertainties in the wavenumber. Thus the wavenumber is modeled as a random variable or a random field. We discretize the Helmholtz equation using finite differences in space, which leads to a linear system of algebraic equations including random variables. A stochastic Galerkin method yields a deterministic linear system of algebraic equations. This linear system is high-dimensional, sparse and complex symmetric but, in general, not hermitian. We therefore solve this system iteratively with GMRES and propose two preconditioners: a complex shifted Laplace preconditioner and a mean value preconditioner. Both preconditioners reduce the number of iteration steps as well as the computation time in our numerical experiments.
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