Abstract
The deflated block conjugate gradient (D-BCG) method is an attractive approach for the solution of symmetric positive definite linear systems with multiple right-hand sides. However, the orthogonality between the block residual vectors and the deflation subspace is gradually lost along with the process of the underlying algorithm implementation, which usually causes the algorithm to be unstable or possibly have delayed convergence. In order to maintain such orthogonality to keep certain level, full reorthogonalization could be employed as a remedy, but the expense required is quite costly. In this paper, we present a new projected variant of the deflated block conjugate gradient (PD-BCG) method to mitigate the loss of this orthogonality, which is helpful to deal with the delay of convergence and thus further achieve the theoretically faster convergence rate of D-BCG. Meanwhile, the proposed PD-BCG method is shown to scarcely have any extra computational cost, while having the same theoretical properties as D-BCG in exact arithmetic. Additionally, an automated reorthogonalization strategy is introduced as an alternative choice for the PD-BCG method. Numerical experiments demonstrate that PD-BCG is more efficient than its counterparts especially when solving ill-conditioned linear systems or linear systems suffering from rank deficiency.



Similar content being viewed by others
References
Arioli, M., Duff, I.S., Ruiz, D., Sadkane, M.: Block Lanczos techniques for accelerating the block Cimmino method. SIAM J. Sci. Comput. 16(6), 1478–1511 (1995)
Abdel-Rehim, A.M., Morgan, R.B., Nicely, D.A., Wilcox, W.: Deflated and restarted symmetric Lanczos methods for eigenvalues and linear equations with multiple right-hand sides. SIAM J. Sci. Comput. 32(1), 129–149 (2010)
Agullo, E., Giraud, L., Jing, Y.-F.: Block GMRES method with inexact breakdowns and deflated restating. SIAM J. Matrix Anal. Appl. 35(4), 1625–1651 (2014)
Aliaga, J.I., Boley, D.L., Freund, R.W., Hernández, V.: A Lanczos-type method for multiple starting vectors. Math. Comput. 69, 1577–1601 (2000)
Bristeau, M.O., Erhel, J.: Augmented conjugate gradient. Application in an iterative process for the solution of scattering problems. Numer. Algorithms 18, 71–90 (1998)
Birk, S.: Deflated shifted block Krylov subspace methods for Hermitian positive definite matrices, Ph.D. thesis. Fakultät für Mathematik und Naturwissenschaften, Bergische Universität Wuppertal (2015)
Birk, S., Frommer, A.: A deflated conjugate gradient method for multiple right hand sides and multiple shifts. Numer. Algorithms 67, 507–529 (2014)
Baglama, J.: Dealing with linear dependence during the iterations of the restarted block Lanczos methods. Numer. Algorithms 25, 23–36 (2000)
Björck, A., Palge, C.: Loss and recapture of orthogonality in the modified Gram–Schmidt algorithm. SIAM J. Matrix Anal. Appl. 13(1), 176–190 (1992)
Chen, J.: A deflated version of the block conjugate gradient algorithm with an application to Gaussian process maximum likelihood estimation. Preprint ANL/MCS-P1927-0811, Argonne National Laboratory, Argonne (2011)
Cockett, R.: The block conjugate gradient for multiple right hand sides in a direct current resistivity inversion. http://www.row1.ca/s/pdfs/courses/BlockCG.pdf. Accessed 28 Feb 2015
Chan, T.-F., Wan, W.-L.: Analysis of projection methods for solving linear systems with multiple right-hand sides. SIAM J. Sci. Comput. 18(6), 1698–1721 (1997)
Chapman, A., Saad, Y.: Deflated and augmented Krylov subspace techniques. Numer. Linear Algebra Appl. 4(1), 43–66 (1997)
Chatfield, D.C., Reeves, M.S., Truhlar, D.G., Duneczky, C., Schwenke, D.W.: Complex generalized minimal residual algorithm for iterative solution of quantum mechanical reactive scattering equations. J. Chem. Phys. 97(11), 8322–8333 (1992)
Davis, T.A., Hu, Y.F.: The University of Florida sparse matrix collection. ACM Trans. Math. Softw. 38(1), 249–260 (2011)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester, New York (2001)
Erhel, J., Guyomarch, F.: An augmented conjugate gradient method for solving consecutive symmetric positive definite linear systems. SIAM J. Matrix Anal. Appl. 21(4), 1279–1299 (2000)
Ekström, S.E., Neytcheva, M.: Enabling the full potential of the deflation techniques? Report. Harbin. In: The 13th International Conference of China Matrix Theory and Its Application (2018)
Gaul, A., Gutknecht, M.H., Liesen, J., Nabben, R.: A framework for deflated and augmented Krylov subspace methods. SIAM J. Matrix Anal. Appl. 34(2), 495–518 (2013)
Gutknecht, M.H.: Block Krylov space methods for linear systems with multiple right-hand sides: an introduction. In: Siddiqi, A.H., Duff, I.S., Christensen, O. (eds.) Modern Mathematical Models, Methods and Algorithms for Real World Systems, pp. 420–447. Anamaya, New Delhi (2007)
Golub, G.H., Ruiz, D., Touhami, A.: A hybrid approach combining Chebyshev filter and conjugate gradient for solving linear systems with multiple right-hand sides. SIAM J. Matrix Anal. Appl. 29(3), 774–795 (2007)
Giraud, L., Langou, J., Rozloznik, M.: The loss of orthogonality in the Gram–Schmidt orthogonalization process. Comput. Math. Appl. 50(7), 1069–1075 (2005)
Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49(6), 409–436 (1952)
Ji, H., Li, Y.-H.: A breakdown-free block conjugate gradient method. BIT Numer. Math. 57, 379–403 (2017)
Kahl, K., Rittich, H.: The deflated conjugate gradient method: convergence, perturbation and accuracy. Linear Algebra Appl. 515, 111–129 (2017)
Morgan, R.B.: A restarted GMRES method augmented with eigenvectors. SIAM J. Matrix Anal. Appl. 16(4), 1154–1171 (1995)
Morgan, R.B.: Restarted block-GMRES with deflation of eigenvalues. Appl. Numer. Math. 54, 222–236 (2005)
Nikishin, A.A., Yeremin, A.Y.: Variable block CG algorithms for solving large sparse symmetric positive definite linear systems on parallel computers, I: general iterative scheme. SIAM J. Matrix Anal. Appl. 16(4), 1135–1153 (1995)
Nicolaides, R.A.: Deflation of conjugate gradients with applications to bundary value problems. SIAM J. Numer. Appl. 24(2), 355–365 (1987)
O’Leary, D.P.: The block conjugate gradient algorithm and related methods. Linear Algebra Appl. 29, 293–322 (1980)
Parks, M.L., de Sturler, E., Mackey, G., Johnson, D.D., Maiti, S.: Recycling Krylov subspaces for sequences of linear systems. SIAM J. Sci. Comput. 28(5), 1651–1674 (2006)
Peng, Z., Shao, Y., Lee, J.-F.: Advanced model order reduction technique in real-life IC/package design. In: IEEE Electrical Design of Advanced Package and Systems Symposium (2010)
Simon, H.D.: The Lanczos algorithm with partial reorthogonalization. Math. Comput. 42(165), 115–142 (1984)
Soodhalter, K.M.: Block Krylov subspace recycling for shifted systems with unrelated right-hand sides. SIAM J. Sci. Comput. 38(1), A302–A324 (2016)
Saad, Y., Yeung, M., Erhel, J., Guyomarch, F.: A deflated version of the conjugate gradient algorithm. SIAM J. Sci. Comput. 21(5), 1909–1926 (2000)
Saad, Y.: Analysis of augmented Krylov subspace methods. SIAM J. Matrix Anal. Appl. 18(2), 435–449 (1997)
Sun, D.-L., Huang, T.-Z., Carpentieri, B., Jing, Y.-F.: Flexible and deflated variants of the block shifted GMRES method. J. Comput. Appl. Math. 345, 168–183 (2019)
Sun, D.-L., Huang, T.-Z., Jing, Y.-F., Carpentieri, B.: A block GMRES method with deflated restarting for solving linear systems with multiple shifts and multiple right-hand sides. Numer. Linear Algebra Appl. 25(5), e2148 (2018)
Sun, D.-L., Carpentieri, B., Huang, T.-Z., Jing, Y.-F.: A spectrally preconditioned and initially deflated variant of the restarted block GMRES method for solving multiple right-hand sides linear systems. Int. J. Mech. Sci. 144, 775–787 (2018)
Sun, D.-L., Huang, T.-Z., Carpentieri, B., Jing, Y.-F.: A new shifted block GMRES method with inexact breakdowns for solving multi-shifted and multiple right-hand sides linear systems. J. Sci. Comput. 78(2), 746–769 (2019)
Xiang, Y.-F., Jing, Y.-F., Huang, T.-Z., Sun, D.-L.: On adaptive restart procedures for the breakdown-free block conjugate gradient method. Report. Harbin. In: The 13th International Conference of China Matrix Theory and Its Application (2018)
Acknowledgements
The authors would like to gratefully thank the anonymous referees for their insightful comments and valuable suggestions, which enabled us to substantially improve the quality of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research is supported by NSFC (61772003) and Science Strength Promotion Programme of UESTC.
Rights and permissions
About this article
Cite this article
Xiang, YF., Jing, YF. & Huang, TZ. A New Projected Variant of the Deflated Block Conjugate Gradient Method. J Sci Comput 80, 1116–1138 (2019). https://doi.org/10.1007/s10915-019-00969-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10915-019-00969-4