Coarse spaces are instrumental in obtaining scalability for domain decomposition methods for partial differential equations (PDEs). However, it is known that most popular choices of coarse spaces perform rather weakly in the presence of heterogeneities in the PDE coefficients, especially for systems of PDEs. Here, we introduce in a variational setting a new coarse space that is robust even when there are such heterogeneities. We achieve this by solving local generalized eigenvalue problems in the overlaps of subdomains that isolate the terms responsible for slow convergence. We prove a general theoretical result that rigorously establishes the robustness of the new coarse space and give some numerical examples on two and three dimensional h...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
Coarse grid correction is a key ingredient in order to have scalable domain decomposition methods. I...
Two-level domain decomposition methods are preconditioned Krylov solvers. What separates one and two...
Coarse spaces are instrumental in obtaining scalability for domain decomposition methods for partial...
Coarse spaces are instrumental in obtaining scalability for domain decomposition methods. However, i...
As many DD methods the two level Additive Schwarz method may suffer from a lack of robustness with r...
In science and engineering, many problems exhibit multiscale properties, making the development of e...
International audienceCoarse grid correction is a key ingredient in order to have scalable domain de...
GenEO (`Generalised Eigenvalue problems on the Overlap') is a method for computing an operator-depen...
A new reduced dimension adaptive GDSW (Generalized Dryja-Smith-Widlund) overlapping Schwarz method f...
A robust two-level overlapping Schwarz method for scalar elliptic model problems with highly varying...
Two-level domain decomposition preconditioners lead to fast convergence and scalability of iterative...
We compare the spectra of local generalized eigenvalue problems in different adaptive coarse spaces ...
Generalized eigenvalue problems on the overlap(GenEO) is a method for computing an operator-dependen...
Two-level overlapping Schwarz methods for elliptic partial differential equations combine local solv...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
Coarse grid correction is a key ingredient in order to have scalable domain decomposition methods. I...
Two-level domain decomposition methods are preconditioned Krylov solvers. What separates one and two...
Coarse spaces are instrumental in obtaining scalability for domain decomposition methods for partial...
Coarse spaces are instrumental in obtaining scalability for domain decomposition methods. However, i...
As many DD methods the two level Additive Schwarz method may suffer from a lack of robustness with r...
In science and engineering, many problems exhibit multiscale properties, making the development of e...
International audienceCoarse grid correction is a key ingredient in order to have scalable domain de...
GenEO (`Generalised Eigenvalue problems on the Overlap') is a method for computing an operator-depen...
A new reduced dimension adaptive GDSW (Generalized Dryja-Smith-Widlund) overlapping Schwarz method f...
A robust two-level overlapping Schwarz method for scalar elliptic model problems with highly varying...
Two-level domain decomposition preconditioners lead to fast convergence and scalability of iterative...
We compare the spectra of local generalized eigenvalue problems in different adaptive coarse spaces ...
Generalized eigenvalue problems on the overlap(GenEO) is a method for computing an operator-dependen...
Two-level overlapping Schwarz methods for elliptic partial differential equations combine local solv...
The convergence rate of domain decomposition methods is generally determined by the eigenvalues of t...
Coarse grid correction is a key ingredient in order to have scalable domain decomposition methods. I...
Two-level domain decomposition methods are preconditioned Krylov solvers. What separates one and two...