Deflated and restarted symmetric Lanczos methods for eigenvalues and linear equations with multiple righthand sides
Abstract
A deflated restarted Lanczos algorithm is given for both solving symmetric linear equations and computing eigenvalues and eigenvectors. The restarting limits the storage so that finding eigenvectors is practical. Meanwhile, the deflating from the presence of the eigenvectors allows the linear equations to generally have good convergence in spite of the restarting. Some reorthogonalization is necessary to control roundoff error, and several approaches are discussed. The eigenvectors generated while solving the linear equations can be used to help solve systems with multiple righthand sides. Experiments are given with large matrices from quantum chromodynamics that have many righthand sides.
 Publication:

arXiv eprints
 Pub Date:
 June 2008
 DOI:
 10.48550/arXiv.0806.3477
 arXiv:
 arXiv:0806.3477
 Bibcode:
 2008arXiv0806.3477A
 Keywords:

 Mathematical Physics;
 High Energy Physics  Lattice
 EPrint:
 20 pages, 13 figures