Analysis of the Finite Precision s-Step Biconjugate Gradient Method

Abstract : We analyze the s-step biconjugate gradient algorithm in nite precision arithmetic and derive a bound for the residual norm in terms of a minimum polynomial of a perturbed matrix multiplied by an ampli cation factor. Our bound enables comparison of s-step and classical biconjugate gradient in terms of ampli cation factors. Our results show that for s-step biconjugate gradient the ampli cation factor depends heavily on the quality of s-step polynomial bases generated in each outer loop.

[1]  W. Joubert,et al.  Parallelizable restarted iterative methods for nonsymmetric linear systems. part I: Theory , 1992 .

[2]  Anthony T. Chronopoulos,et al.  s-step iterative methods for symmetric linear systems , 1989 .

[3]  James Demmel,et al.  Avoiding Communication in Nonsymmetric Lanczos-Based Krylov Subspace Methods , 2013, SIAM J. Sci. Comput..

[4]  G. Meurant,et al.  The Lanczos and conjugate gradient algorithms in finite precision arithmetic , 2006, Acta Numerica.

[5]  Dennis Gannon,et al.  On the Impact of Communication Complexity on the Design of Parallel Numerical Algorithms , 1984, IEEE Transactions on Computers.

[6]  Qiang Ye,et al.  Analysis of the finite precision bi-conjugate gradient algorithm for nonsymmetric linear systems , 2000, Math. Comput..

[7]  Sivan Toledo,et al.  Quantitative performance modeling of scientific computations and creating locality in numerical algorithms , 1995 .

[8]  Qiang Ye,et al.  Residual Replacement Strategies for Krylov Subspace Iterative Methods for the Convergence of True Residuals , 2000, SIAM J. Sci. Comput..

[9]  L. Reichel,et al.  A Newton basis GMRES implementation , 1994 .

[10]  Eric de Sturler,et al.  A Performance Model for Krylov Subspace Methods on Mesh-Based Parallel Computers , 1996, Parallel Comput..

[11]  James Demmel,et al.  Minimizing communication in sparse matrix solvers , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[12]  Anthony T. Chronopoulos,et al.  Parallel Iterative S-Step Methods for Unsymmetric Linear Systems , 1996, Parallel Comput..

[13]  Gerard L. G. Sleijpen,et al.  Reliable updated residuals in hybrid Bi-CG methods , 1996, Computing.

[14]  Anne Greenbaum,et al.  Predicting the Behavior of Finite Precision Lanczos and Conjugate Gradient Computations , 2015, SIAM J. Matrix Anal. Appl..

[15]  C. Paige Accuracy and effectiveness of the Lanczos algorithm for the symmetric eigenproblem , 1980 .

[16]  Mark Hoemmen,et al.  Communication-avoiding Krylov subspace methods , 2010 .

[17]  H. V. der Residual Replacement Strategies for Krylov Subspace Iterative Methods for the Convergence of True Residuals , 2000 .

[18]  Anthony T. Chronopoulos,et al.  On the efficient implementation of preconditioned s-step conjugate gradient methods on multiprocessors with memory hierarchy , 1989, Parallel Comput..

[19]  H. Walker Implementation of the GMRES method using householder transformations , 1988 .

[20]  J. Vanrosendale,et al.  Minimizing inner product data dependencies in conjugate gradient iteration , 1983 .

[21]  Sivan Toledo,et al.  Efficient out-of-core algorithms for linear relaxation using blocking covers , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.

[22]  Jens-Peter M. Zemke,et al.  Krylov Subspace Methods in Finite Precision : A Unified Approach , 2003 .

[23]  Marghoob Mohiyuddin,et al.  Tuning Hardware and Software for Multiprocessors , 2012 .

[24]  G. Meurant The Lanczos and Conjugate Gradient Algorithms: From Theory to Finite Precision Computations , 2006 .

[25]  J. Demmel,et al.  Avoiding Communication in Computing Krylov Subspaces , 2007 .

[26]  H. Walker,et al.  Note on a Householder implementation of the GMRES method , 1986 .

[27]  Gene H. Golub,et al.  Matrix computations , 1983 .

[28]  Graham F. Carey,et al.  Parallelizable Restarted Iterative Methods for Nonsymmetric Linear Systems , 1991, PPSC.

[29]  Miroslav Rozlozník,et al.  Modified Gram-Schmidt (MGS), Least Squares, and Backward Stability of MGS-GMRES , 2006, SIAM J. Matrix Anal. Appl..

[30]  A. Greenbaum Estimating the Attainable Accuracy of Recursively Computed Residual Methods , 1997, SIAM J. Matrix Anal. Appl..

[31]  C. T. Fike,et al.  Norms and exclusion theorems , 1960 .