Breakdowns and stagnation in iterative methods

被引:0
作者
Zbigniew Leyk
机构
[1] Texas A&M University,Institute for Scientific Computation
来源
BIT Numerical Mathematics | 1997年 / 37卷
关键词
65F10; Iterative methods; Krylov subspace; biconjugate gradient; conjugate gradient; GMRES; generalized conjugate residual;
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学科分类号
摘要
One of the disadvantages of Krylov subspace iterative methods is the possibility of breakdown. This occurs when it is impossible to get the next approximation of the solution to the linear system of equationsAu=f. There are two different situations: lucky breakdown, when we have found the solution and hard breakdown, when the next Krylov subspace cannot be generated and/or the next approximate solution (iterate) cannot be computed. We show that some breakdowns depend on the chosen method of generating the basis vectors. Another undesirable feature of the iterative methods is stagnation. This occurs when the error does not change for several iterative steps. We investigate when iterative methods can stagnate and describe conditions which characterize stagnation. We show that in some cases stagnation can imply breakdown.
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页码:377 / 403
页数:26
相关论文
共 51 条
[1]  
Ashby S.(1990)A taxonomy for conjugate gradient methods SIAM J. Numer. Anal. 27 1542-1568
[2]  
Manteuffel T.(1992)A breakdown-free Lanczos type algorithm for solving linear systems Numer. Math. 63 29-38
[3]  
Saylor P.(1991)A theoretical comparison of the Arnoldi and GMRES algorithms SIAM J. Sci. Stat. Comput. 12 58-78
[4]  
Brezinski C.(1983)Variational iterative methods for nonsymmetric systems of linear equations SIAM J. Numer. Anal. 20 345-357
[5]  
Zaglia M. Redivo(1984)Necessary and sufficient conditions for the existence of a conjugate gradient method SIAM J. Numer. Anal. 21 352-362
[6]  
Sadok H.(1987)Orthogonal error methods SIAM J. Numer. Anal. 24 170-187
[7]  
Brown P.(1991)Iterative solution of linear systems Acta Numerica 1 57-100
[8]  
Eisenstat S.(1993)An implementation of the look-ahead Lanczos algorithm for non-Hermitian matrices SIAM J. Sci. Comput. 14 137-158
[9]  
Elman H.(1991)QMR: a quasi minimal residual method for non-Hermitian linear systems Numer. Math. 60 315-339
[10]  
Schultz M.(1989)Some history of the conjugate gradient and Lanczos methods SIAM Rev. 31 50-102