TY - JOUR
T1 - The DEFLATED-GMRES(m, k) method with switching the restart frequency dynamically
AU - Moriya, Kentaro
AU - Nodera, Takashi
PY - 2000/1/1
Y1 - 2000/1/1
N2 - The DEFLATED-GMRES(m, k) method is one of the major iterative solvers for the large sparse linear systems of equations, Ax = b. This algorithm assembles a preconditioner adaptively for the GMRES(m) method based on eigencomponents gathered from the Arnoldi process during iterations. It is usually known that if a restarted GMRES(m) method is used to solve linear systems of equations, the information of the smallest eigencomponents is lost at each restart and the super-linear convergence may also be lost. In this paper, we propose an adaptive procedure that combines the DEFLATED-GMRES(m, k) algorithm and the determination of a restart frequency m automatically. It is shown that a new algorithm combining elements of both will reduce the negative effects of the restarted procedure. The numerical experiments are presented on three test problems by using the MIMD parallel machine AP3000. From these numerical results, we show that the proposed algorithm leads to faster convergence than the conventional DEFLATED-GMRES(m, k) method.
AB - The DEFLATED-GMRES(m, k) method is one of the major iterative solvers for the large sparse linear systems of equations, Ax = b. This algorithm assembles a preconditioner adaptively for the GMRES(m) method based on eigencomponents gathered from the Arnoldi process during iterations. It is usually known that if a restarted GMRES(m) method is used to solve linear systems of equations, the information of the smallest eigencomponents is lost at each restart and the super-linear convergence may also be lost. In this paper, we propose an adaptive procedure that combines the DEFLATED-GMRES(m, k) algorithm and the determination of a restart frequency m automatically. It is shown that a new algorithm combining elements of both will reduce the negative effects of the restarted procedure. The numerical experiments are presented on three test problems by using the MIMD parallel machine AP3000. From these numerical results, we show that the proposed algorithm leads to faster convergence than the conventional DEFLATED-GMRES(m, k) method.
KW - AP3000
KW - Automatic restart
KW - Deflated-GMRES
KW - Preconditioning
UR - http://www.scopus.com/inward/record.url?scp=0034368171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0034368171&partnerID=8YFLogxK
U2 - 10.1002/1099-1506(200010/12)7:7/8<569::AID-NLA213>3.0.CO;2-8
DO - 10.1002/1099-1506(200010/12)7:7/8<569::AID-NLA213>3.0.CO;2-8
M3 - Article
AN - SCOPUS:0034368171
SN - 1070-5325
VL - 7
SP - 569
EP - 584
JO - Numerical Linear Algebra with Applications
JF - Numerical Linear Algebra with Applications
IS - 7-8
ER -