Solving large-scale sparse eigenvalue problems and linear systems of equations for accelerator modeling
- SLAC National Accelerator Lab
The solutions of sparse eigenvalue problems and linear systems constitute one of the key computational kernels in the discretization of partial differential equations for the modeling of linear accelerators. The computational challenges faced by existing techniques for solving those sparse eigenvalue problems and linear systems call for continuing research to improve on the algorithms so that ever increasing problem size as required by the physics application can be tackled. Under the support of this award, the filter algorithm for solving large sparse eigenvalue problems was developed at Stanford to address the computational difficulties in the previous methods with the goal to enable accelerator simulations on then the world largest unclassified supercomputer at NERSC for this class of problems. Specifically, a new method, the Hemitian skew-Hemitian splitting method, was proposed and researched as an improved method for solving linear systems with non-Hermitian positive definite and semidefinite matrices.
- Research Organization:
- Gene Golub, Stanford University
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- FC02-01ER41177
- OSTI ID:
- 950471
- Report Number(s):
- DOE/ER/41177-F
- Country of Publication:
- United States
- Language:
- English
Similar Records
Algorithms for sparse matrix eigenvalue problems. [DBLKLN, block Lanczos algorithm with local reorthogonalization strategy]
Generalization of Davidson's method for computing eigenvalues of sparse symmetric matrices