Name
Title | ||
|---|---|---|
Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization. |
Abstract | ||
|---|---|---|
Fast computation of demagnetization curves is essential for the computational design of soft magnetic sensors or permanent magnet materials. We show that a sparse preconditioner for a nonlinear conjugate gradient energy minimizer can lead to a speed up by a factor of 3 and 7 for computing hysteresis in soft magnetic and hard magnetic materials, respectively. As a preconditioner an approximation of the Hessian of the Lagrangian is used, which only takes local field terms into account. Preconditioning requires a few additional sparse matrix vector multiplications per iteration of the nonlinear conjugate gradient method, which is used for minimizing the energy for a given external field. The time to solution for computing the demagnetization curve scales almost linearly with problem size. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cpc.2018.09.004 | Computer Physics Communications |
Keywords | Field | DocType |
Preconditioning,Ground state computation,Micromagnetics,GPU acceleration,Energy minimization | Preconditioner,Mathematical analysis,Magnet,Hessian matrix,Nonlinear conjugate gradient method,Demagnetizing field,Mathematics,Local field,Energy minimization,Computation | Journal |
Volume | ISSN | Citations |
235 | 0010-4655 | 0 |
PageRank | References | Authors |
0.34 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Exl | 1 | 14 | 4.79 |
| Johann Fischbacher | 2 | 1 | 1.11 |
| Alexander Kovacs | 3 | 1 | 1.11 |
| Harald Oezelt | 4 | 3 | 1.19 |
| Markus Gusenbauer | 5 | 16 | 4.29 |
| Thomas Schrefl | 6 | 7 | 3.08 |