IDR(s): A Family of Simple and Fast Algorithms for Solving Large Nonsymmetric Systems of Linear Equations

We present IDR($s$), a new family of efficient, short-recurrence methods for large nonsymmetric systems of linear equations. The new methods are based on the induced dimension reduction (IDR) method proposed by Sonneveld in 1980. IDR($s$) generates residuals that are forced to be in a sequence of nested subspaces. Although IDR($s$) behaves like an iterative method, in exact arithmetic it computes the true solution using at most $N + N/s$ matrix-vector products, with $N$ the problem size and $s$ the codimension of a fixed subspace. We describe the algorithm and the underlying theory and present numerical experiments to illustrate the theoretical properties of the method and its performance for systems arising from different applications. Our experiments show that IDR($s$) is competitive with or superior to most Bi-CG-based methods and outperforms Bi-CGSTAB when $s > 1$.

[1]  Cornelis Vuik,et al.  On the Construction of Deflation-Based Preconditioners , 2001, SIAM J. Sci. Comput..

[2]  Martin H. Gutknecht,et al.  Variants of BICGSTAB for Matrices with Complex Spectrum , 1993, SIAM J. Sci. Comput..

[3]  G. Golub,et al.  Gmres: a Generalized Minimum Residual Algorithm for Solving , 2022 .

[4]  Sol Hellerman,et al.  Normal Monthly Wind Stress Over the World Ocean with Error Estimates , 1983 .

[5]  R. Fletcher Conjugate gradient methods for indefinite systems , 1976 .

[6]  Gerard L. G. Sleijpen,et al.  Maintaining convergence properties of BiCGstab methods in finite precision arithmetic , 1995, Numerical Algorithms.

[7]  C. Lanczos Solution of Systems of Linear Equations by Minimized Iterations1 , 1952 .

[8]  Henk A. van der Vorst,et al.  Bi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems , 1992, SIAM J. Sci. Comput..

[9]  Shao-Liang Zhang,et al.  GPBi-CG: Generalized Product-type Methods Based on Bi-CG for Solving Nonsymmetric Linear Systems , 1997, SIAM J. Sci. Comput..

[10]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[11]  Y. Saad,et al.  GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems , 1986 .

[12]  P. Sonneveld CGS, A Fast Lanczos-Type Solver for Nonsymmetric Linear systems , 1989 .

[13]  M. B. Van Gijzen,et al.  A finite element discretization for stream-function problems on multiply connected domains , 1997 .

[14]  R. Nicolaides Deflation of conjugate gradients with applications to boundary value problems , 1987 .

[15]  P. Wesseling,et al.  Numerical experiments with a multiple grid and a preconditioned Lanczos type method , 1980 .

[16]  Tony F. Chan,et al.  ML(k)BiCGSTAB: A BiCGSTAB Variant Based on Multiple Lanczos Starting Vectors , 1999, SIAM J. Sci. Comput..

[17]  Roland W. Freund,et al.  A Transpose-Free Quasi-Minimal Residual Algorithm for Non-Hermitian Linear Systems , 1993, SIAM J. Sci. Comput..

[18]  R. Freund,et al.  QMR: a quasi-minimal residual method for non-Hermitian linear systems , 1991 .

[19]  T. Manteuffel,et al.  Necessary and Sufficient Conditions for the Existence of a Conjugate Gradient Method , 1984 .