Startseite Mathematik Dimensionally Consistent Preconditioning for Saddle-Point Problems
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Dimensionally Consistent Preconditioning for Saddle-Point Problems

  • Roland Herzog ORCID logo EMAIL logo
Veröffentlicht/Copyright: 18. Mai 2021

Abstract

The preconditioned iterative solution of large-scale saddle-point systems is of great importance in numerous application areas, many of them involving partial differential equations. Robustness with respect to certain problem parameters is often a concern, and it can be addressed by identifying proper scalings of preconditioner building blocks. In this paper, we consider a new perspective to finding effective and robust preconditioners. Our approach is based on the consideration of the natural physical units underlying the respective saddle-point problem. This point of view, which we refer to as dimensional consistency, suggests a natural combination of the parameters intrinsic to the problem. It turns out that the scaling obtained in this way leads to robustness with respect to problem parameters in many relevant cases. As a consequence, we advertise dimensional consistency based preconditioning as a new and systematic way to designing parameter robust preconditoners for saddle-point systems arising from models for physical phenomena.

References

[1] I. Babuška and M. Suri, Locking effects in the finite element approximation of elasticity problems, Numer. Math. 62 (1992), no. 4, 439–463. 10.1007/BF01396238Suche in Google Scholar

[2] M. Benzi, G. H. Golub and J. Liesen, Numerical solution of saddle point problems, Acta Numer. 14 (2005), 1–137. 10.1017/S0962492904000212Suche in Google Scholar

[3] H. C. Elman, D. J. Silvester and A. J. Wathen, Finite Elements and Fast Iterative Solvers: With Applications in Incompressible Fluid Dynamics, 2nd ed., Numer. Math. Sci. Comput., Oxford University, Oxford, 2014. 10.1093/acprof:oso/9780199678792.001.0001Suche in Google Scholar

[4] O. L. S. Elvetun and B. R. F. Nielsen, PDE-constrained optimization with local control and boundary observations: Robust preconditioners, SIAM J. Sci. Comput. 38 (2016), no. 6, A3461–A3491. 10.1137/140999098Suche in Google Scholar

[5] V. Girault and P.-A. Raviart, Finite Element Methods for Navier–Stokes Equations, Springer Ser. Comput. Math. 5, Springer, Berlin, 1986. 10.1007/978-3-642-61623-5Suche in Google Scholar

[6] A. Günnel, R. Herzog and E. Sachs, A note on preconditioners and scalar products in Krylov subspace methods for self-adjoint problems in Hilbert space, Electron. Trans. Numer. Anal. 41 (2014), 13–20. Suche in Google Scholar

[7] R. Herzog and K. M. Soodhalter, SUBMINRES. A modified implementation of MINRES to monitor residual subvector norms for block systems, DOI: 10.5281/zenodo.47393. Suche in Google Scholar

[8] R. Herzog and K. M. Soodhalter, A modified implementation of MINRES to monitor residual subvector norms for block systems, SIAM J. Sci. Comput. 39 (2017), no. 6, A2645–A2663. 10.1137/16M1093021Suche in Google Scholar

[9] R. Hiptmair, Operator preconditioning, Comput. Math. Appl. 52 (2006), no. 5, 699–706. 10.1016/j.camwa.2006.10.008Suche in Google Scholar

[10] A. Klawonn, An optimal preconditioner for a class of saddle point problems with a penalty term, SIAM J. Sci. Comput. 19 (1998), no. 2, 540–552. 10.1137/S1064827595279575Suche in Google Scholar

[11] A. Klawonn, Block-triangular preconditioners for saddle point problems with a penalty term, SIAM J. Sci. Comput. 19 (1998), 172–184. 10.1137/S1064827596303624Suche in Google Scholar

[12] M. Kollmann and W. Zulehner, A robust preconditioner for distributed optimal control for Stokes flow with control constraints, Numerical Mathematics and Advanced Applications—ENUMATH 2011, Springer, Heidelberg (2013), 771–779. 10.1007/978-3-642-33134-3_81Suche in Google Scholar

[13] M. Kuchta, K.-A. Mardal and M. Mortensen, On the singular Neumann problem in linear elasticity, Numer. Linear Algebra Appl. 26 (2019), no. 1, Article ID e2212. 10.1002/nla.2212Suche in Google Scholar

[14] A. Logg, K.-A. Mardal and G. N. Wells, Automated Solution of Differential Equations by the Finite Element Method, Lect. Notes Comput. Sci. Eng. 84, Springer, Heidelberg, 2012. 10.1007/978-3-642-23099-8Suche in Google Scholar

[15] K.-A. Mardal, B. R. F. Nielsen and M. Nordaas, Robust preconditioners for PDE-constrained optimization with limited observations, BIT 57 (2017), no. 2, 405–431. 10.1007/s10543-016-0635-8Suche in Google Scholar

[16] K.-A. Mardal and R. Winther, Preconditioning discretizations of systems of partial differential equations, Numer. Linear Algebra Appl. 18 (2011), no. 1, 1–40. 10.1002/nla.716Suche in Google Scholar

[17] J. E. Marsden and T. J. R. Hughes, Mathematical Foundations of Elasticity, Dover, New York, 1994. Suche in Google Scholar

[18] C. C. Paige and M. A. Saunders, Solutions of sparse indefinite systems of linear equations, SIAM J. Numer. Anal. 12 (1975), no. 4, 617–629. 10.1137/0712047Suche in Google Scholar

[19] J. W. Pearson, M. Stoll and A. J. Wathen, Regularization-robust preconditioners for time-dependent PDE-constrained optimization problems, SIAM J. Matrix Anal. Appl. 33 (2012), no. 4, 1126–1152. 10.1137/110847949Suche in Google Scholar

[20] J. Pestana and A. J. Wathen, Natural preconditioning and iterative methods for saddle point systems, SIAM Rev. 57 (2015), no. 1, 71–91. 10.1137/130934921Suche in Google Scholar

[21] J. Schöberl and W. Zulehner, Symmetric indefinite preconditioners for saddle point problems with applications to PDE-constrained optimization problems, SIAM J. Matrix Anal. Appl. 29 (2007), no. 3, 752–773. 10.1137/060660977Suche in Google Scholar

[22] D. J. Silvester and V. Simoncini, An optimal iterative solver for symmetric indefinite systems stemming from mixed approximation, ACM Trans. Math. Software 37 (2011), no. 4, 1–22. 10.1145/1916461.1916466Suche in Google Scholar

[23] F. Tröltzsch Optimal Control of Partial Differential Equations, Grad. Stud. Math. 112, American Mathematical Society, Providence, 2010. 10.1090/gsm/112Suche in Google Scholar

[24] M. ur Rehman, T. Geenen, C. Vuik, G. Segal and S. P. MacLachlan, On iterative methods for the incompressible Stokes problem, Internat. J. Numer. Methods Fluids 65 (2011), no. 10, 1180–1200. 10.1002/fld.2235Suche in Google Scholar

[25] R. Verfürth, Error estimates for a mixed finite element approximation of the Stokes equations, RAIRO Anal. Numér. 18 (1984), no. 2, 175–182. 10.1051/m2an/1984180201751Suche in Google Scholar

[26] A. Wathen, Preconditioning and convergence in the right norm, Int. J. Comput. Math. 84 (2007), no. 8, 1199–1209. 10.1080/00207160701355961Suche in Google Scholar

[27] A. Wathen and D. Silvester, Fast iterative solution of stabilised Stokes systems. I. Using simple diagonal preconditioners, SIAM J. Numer. Anal. 30 (1993), no. 3, 630–649. 10.1137/0730031Suche in Google Scholar

[28] A. J. Wathen, Preconditioning, Acta Numer. 24 (2015), 329–376. 10.1017/S0962492915000021Suche in Google Scholar

[29] C. Wieners, Robust multigrid methods for nearly incompressible elasticity, Computing 64 (2000), no. 4, 289–306. 10.1007/s006070070026Suche in Google Scholar

[30] C. Wieners, Taylor–Hood elements in 3D, Analysis and Simulation of Multifield Problems, Springer, Berlin (2003), 189–196. 10.1007/978-3-540-36527-3_21Suche in Google Scholar

[31] W. Zulehner, Nonstandard norms and robust estimates for saddle point problems, SIAM J. Matrix Anal. Appl. 32 (2011), no. 2, 536–560. 10.1137/100814767Suche in Google Scholar

Received: 2020-03-20
Revised: 2021-04-14
Accepted: 2021-04-21
Published Online: 2021-05-18
Published in Print: 2021-07-01

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 26.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/cmam-2020-0037/html
Button zum nach oben scrollen