Algorithm 913

Algorithm 913: An elegant IDR(s) variant that efficiently exploits biorthogonality properties. The IDR(s) method that is proposed in Sonneveld and van Gijzen [2008] is a very efficient limited memory method for solving large nonsymmetric systems of linear equations. IDR(s) is based on the induced dimension reduction theorem, that provides a way to construct subsequent residuals that lie in a sequence of shrinking subspaces. The IDR(s) algorithm that is given in Sonneveld and van Gijzen [2008] is a direct translation of the theorem into an algorithm. This translation is not unique. This article derives a new IDR(s) variant, that imposes (one-sided) biorthogonalization conditions on the iteration vectors. The resulting method has lower overhead in vector operations than the original IDR(s) algorithms. In exact arithmetic, both algorithms give the same residual at every (s + 1)-st step, but the intermediate residuals and also the numerical properties differ. We show through numerical experiments that the new variant is more stable and more accurate than the original IDR(s) algorithm, and that it outperforms other state-of-the-art techniques for realistic test problems.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 14 articles )

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  1. Zemke, Jens-Peter M.: Variants of IDR with partial orthonormalization (2017)
  2. Astudillo, R.; van Gijzen, M.B.: A restarted induced dimension reduction method to approximate eigenpairs of large unsymmetric matrices (2016)
  3. Il’in, V.P.: Problems of parallel solution of large systems of linear algebraic equations (2016)
  4. Sangers, Alex; van Gijzen, Martin B.: The eigenvectors corresponding to the second eigenvalue of the google matrix and their relation to link spamming (2015)
  5. Aihara, Kensuke; Abe, Kuniyoshi; Ishiwata, Emiko: A quasi-minimal residual variant of IDRstab using the residual smoothing technique (2014)
  6. Elman, Howard C.; Ramage, Alison; Silvester, David J.: IFISS: A computational laboratory for investigating incompressible flow problems (2014)
  7. Gutknecht, Martin H.: Deflated and augmented Krylov subspace methods: A framework for deflated BiCG and related solvers (2014)
  8. Paige, Christopher C.; Panayotov, Ivo; Zemke, Jens-Peter M.: An augmented analysis of the perturbed two-sided Lanczos tridiagonalization process (2014)
  9. Sun, Dong-Lin; Jing, Yan-Fei; Huang, Ting-Zhu; Carpentieri, Bruno: A quasi-minimal residual variant of the BICORSTAB method for nonsymmetric linear systems (2014)
  10. Yeung, Man-Chung: ML($n$)BiCGStabt: a ML($n$)BiCGStab variant with $\bold A$-transpose (2014)
  11. Rendel, Olaf; Rizvanolli, Anisa; Zemke, Jens-Peter M.: IDR: a new generation of Krylov subspace methods? (2013)
  12. Vannieuwenhoven, Nick; Meerbergen, Karl: IMF: an incomplete multifrontal $LU$-factorization for element-structured sparse linear systems (2013)
  13. Knibbe, H.; Oosterlee, C.W.; Vuik, C.: GPU implementation of a Helmholtz Krylov solver preconditioned by a shifted Laplace multigrid method (2011)
  14. van Gijzen, Martin B.; Sonneveld, Peter: Algorithm 913: An elegant $\mathrmIDR(s)$ variant that efficiently exploits biorthogonality properties (2011)