Name
Abstract | ||
|---|---|---|
We consider an automatic construction of locally optimal preconditioners for positive definite linear systems. To achieve this goal, we introduce a differentiable loss function that does not explicitly include the estimation of minimal eigenvalue. Nevertheless, the resulting optimization problem is equivalent to a direct minimization of the condition number. To demonstrate our approach, we construct a parametric family of modified BPX preconditioners. Namely, we define a set of empirical basis functions for coarse finite element spaces and tune them to achieve better condition number. For considered model equations (that includes Poisson, Helmholtz, Convection–diffusion, Biharmonic, and others), we achieve from two to twenty times smaller condition numbers for symmetric positive definite linear systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cam.2021.113811 | Journal of Computational and Applied Mathematics |
Keywords | DocType | Volume |
Multigrid,Boundary value problems,PDE,BPX,Preconditioning,Numerical linear algebra | Journal | 402 |
ISSN | Citations | PageRank |
0377-0427 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivan V. Oseledets | 1 | 306 | 41.96 |
| Vladimir Fanaskov | 2 | 0 | 0.34 |