Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter February 5, 2021

Contractive Local Adaptive Smoothing Based on Dörfler’s Marking in A-Posteriori-Steered p-Robust Multigrid Solvers

  • Ani Miraçi EMAIL logo , Jan Papež and Martin Vohralík

Abstract

In this work, we study a local adaptive smoothing algorithm for a-posteriori-steered p-robust multigrid methods. The solver tackles a linear system which is generated by the discretization of a second-order elliptic diffusion problem using conforming finite elements of polynomial order p 1 . After one V-cycle (“full-smoothing” substep) of the solver of [A. Miraçi, J. Papež, and M. Vohralík, A-posteriori-steered p-robust multigrid with optimal step-sizes and adaptive number of smoothing steps, SIAM J. Sci. Comput. 2021, 10.1137/20M1349503], we dispose of a reliable, efficient, and localized estimation of the algebraic error. We use this existing result to develop our new adaptive algorithm: thanks to the information of the estimator and based on a bulk-chasing criterion, cf. [W. Dörfler, A convergent adaptive algorithm for Poisson’s equation, SIAM J. Numer. Anal. 33 1996, 3, 1106–1124], we mark patches of elements with increased estimated error on all levels. Then, we proceed by a modified and cheaper V-cycle (“adaptive-smoothing” substep), which only applies smoothing in the marked regions. The proposed adaptive multigrid solver picks autonomously and adaptively the optimal step-size per level as in our previous work but also the type of smoothing per level (weighted restricted additive or additive Schwarz) and concentrates smoothing to marked regions with high error. We prove that, under a numerical condition that we verify in the algorithm, each substep (full and adaptive) contracts the error p-robustly, which is confirmed by numerical experiments. Moreover, the proposed algorithm behaves numerically robustly with respect to the number of levels as well as to the diffusion coefficient jump for a uniformly-refined hierarchy of meshes.

Award Identifier / Grant number: 647134 GATIPOR

Funding statement: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 647134 GATIPOR). The work of J. Papež was supported by the Grant Agency of the Czech Republic under Grant No. 20-01074S. The authors are grateful to Inria Sophia Antipolis – Méditerranée “NEF” computation cluster for providing resources and support.

Acknowledgements

We would like to thank the anonymous referees for their useful suggestions concerning this manuscript.

References

[1] A. Anciaux-Sedrakian, L. Grigori, Z. Jorti, J. Papež and S. Yousef, Adaptive solution of linear systems of equations based on a posteriori error estimators, Numer. Algorithms 84 (2020), no. 1, 331–364. 10.1007/s11075-019-00757-zSearch in Google Scholar

[2] D. Bai and A. Brandt, Local mesh refinement multilevel techniques, SIAM J. Sci. Statist. Comput. 8 (1987), no. 2, 109–134. 10.1137/0908025Search in Google Scholar

[3] C. Bernardi and Y. Maday, Spectral methods, Handbook of Numerical Analysis. Vol. V, North-Holland, Amsterdam (1997), 209–485. 10.1016/S1570-8659(97)80003-8Search in Google Scholar

[4] S. C. Brenner and L. R. Scott, The Mathematical Theory of Finite Element Methods, 3rd ed., Texts Appl. Math. 15, Springer, New York, 2008. 10.1007/978-0-387-75934-0Search in Google Scholar

[5] M. Brezina, R. Falgout, S. MacLachlan, T. Manteuffel, S. McCormick and J. Ruge, Adaptive algebraic multigrid, SIAM J. Sci. Comput. 27 (2006), no. 4, 1261–1286. 10.1137/040614402Search in Google Scholar

[6] W. L. Briggs, V. E. Henson and S. F. McCormick, A Multigrid Tutorial, 2nd ed., Society for Industrial and Applied Mathematics, Philadelphia, 2000. 10.1137/1.9780898719505Search in Google Scholar

[7] X.-C. Cai and M. Sarkis, A restricted additive Schwarz preconditioner for general sparse linear systems, SIAM J. Sci. Comput. 21 (1999), no. 2, 792–797. 10.1137/S106482759732678XSearch in Google Scholar

[8] L. Chen, R. H. Nochetto and J. Xu, Optimal multilevel methods for graded bisection grids, Numer. Math. 120 (2012), no. 1, 1–34. 10.1007/s00211-011-0401-4Search in Google Scholar

[9] P. G. Ciarlet, The Finite Element Method for Elliptic Problems, Stud. Math. Appl. 4, North-Holland Publishing, Amsterdam, 1978. 10.1115/1.3424474Search in Google Scholar

[10] V. Dolean, P. Jolivet and F. Nataf, An Introduction to Domain Decomposition Methods. Algorithms, Theory, and Parallel Implementation, Society for Industrial and Applied Mathematics, Philadelphia, 2015. 10.1137/1.9781611974065Search in Google Scholar

[11] W. Dörfler, A convergent adaptive algorithm for Poisson’s equation, SIAM J. Numer. Anal. 33 (1996), no. 3, 1106–1124. 10.1137/0733054Search in Google Scholar

[12] E. Efstathiou and M. J. Gander, Why restricted additive Schwarz converges faster than additive Schwarz, BIT 43 (2003), 945–959. 10.1023/B:BITN.0000014563.33622.1dSearch in Google Scholar

[13] A. Ern and J.-L. Guermond, Theory and Practice of Finite Elements, Appl. Math. Sci. 159, Springer, New York, 2004. 10.1007/978-1-4757-4355-5Search in Google Scholar

[14] M. Griebel and P. Oswald, On the abstract theory of additive and multiplicative Schwarz algorithms, Numer. Math. 70 (1995), no. 2, 163–180. 10.1007/s002110050115Search in Google Scholar

[15] W. Hackbusch, Multigrid Methods and Applications, Springer Ser. Comput. Math. 4, Springer, Berlin, 1985. 10.1007/978-3-662-02427-0Search in Google Scholar

[16] X. Hu, J. Lin and L. T. Zikatanov, An adaptive multigrid method based on path cover, SIAM J. Sci. Comput. 41 (2019), no. 5, S220–S241. 10.1137/18M1194493Search in Google Scholar

[17] B. Janssen and G. Kanschat, Adaptive multilevel methods with local smoothing for H 1 - and H curl -conforming high order finite element methods, SIAM J. Sci. Comput. 33 (2011), no. 4, 2095–2114. 10.1137/090778523Search in Google Scholar

[18] S. Loisel, R. Nabben and D. B. Szyld, On hybrid multigrid-Schwarz algorithms, J. Sci. Comput. 36 (2008), no. 2, 165–175. 10.1007/s10915-007-9183-3Search in Google Scholar

[19] H. Lötzbeyer and U. Rüde, Patch-adaptive multilevel iteration, BIT Numer. Math. 37 (1997), 739–758, 10.1007/BF02510250Search in Google Scholar

[20] S. F. McCormick, Multilevel Adaptive Methods for Partial Differential Equations, Front. Appl. Math. 6, Society for Industrial and Applied Mathematics, Philadelphia, 1989. 10.1137/1.9781611971026Search in Google Scholar

[21] A. Miraçi, J. Papež and M. Vohralík, A multilevel algebraic error estimator and the corresponding iterative solver with p-robust behavior, SIAM J. Numer. Anal. 58 (2020), no. 5, 2856–2884. 10.1137/19M1275929Search in Google Scholar

[22] A. Miraçi, J. Papež and M. Vohralík, A-posteriori-steered p-robust multigrid with optimal step-sizes and adaptive number of smoothing steps, SIAM J. Sci. Comput. (2021), 10.1137/20M1349503. 10.1137/20M1349503Search in Google Scholar

[23] W. F. Mitchell, Adaptive refinement for arbitrary finite-element spaces with hierarchical bases, J. Comput. Appl. Math. 36 (1991), no. 1, 65–78. 10.1016/0377-0427(91)90226-ASearch in Google Scholar

[24] P. Oswald, Multilevel Finite Element Approximation. Theory and Applications, Teubner Skript. Numerik, B. G. Teubner, Stuttgart, 1994. 10.1007/978-3-322-91215-2Search in Google Scholar

[25] J. Papež, U. Rüde, M. Vohralík and B. Wohlmuth, Sharp algebraic and total a posteriori error bounds for h and p finite elements via a multilevel approach. Recovering mass balance in any situation, Comput. Methods Appl. Mech. Engrg. 371 (2020), Article ID 113243. 10.1016/j.cma.2020.113243Search in Google Scholar

[26] J. Papež, Z. Strakoš and M. Vohralík, Estimating and localizing the algebraic and total numerical errors using flux reconstructions, Numer. Math. 138 (2018), no. 3, 681–721. 10.1007/s00211-017-0915-5Search in Google Scholar

[27] A. Quarteroni and A. Valli, Domain Decomposition Methods for Partial Differential Equations, Numer. Math. Sci. Comput., The Clarendon Press, New York, 1999. Search in Google Scholar

[28] U. Rüde, Mathematical and Computational Techniques for Multilevel Adaptive Methods, Front. Appl. Math. 13, Society for Industrial and Applied Mathematics, Philadelphia, 1993. 10.1137/1.9781611970968Search in Google Scholar

[29] J. Schöberl, J. M. Melenk, C. Pechstein and S. Zaglmayr, Additive Schwarz preconditioning for p-version triangular and tetrahedral finite elements, IMA J. Numer. Anal. 28 (2008), no. 1, 1–24. 10.1093/imanum/drl046Search in Google Scholar

[30] E. G. Sewell, Automatic Generation of Triangulations for Piecewise Polynomial Approximation, ProQuest LLC, Ann Arbor, 1972; Ph.D. thesis, Purdue University, 1972. Search in Google Scholar

[31] P. Šolín, K. Segeth and I. Doležel, Higher-Order Finite Element Methods, Stud. Adv. Math., Chapman & Hall/CRC, Boca Raton, 2004. 10.1201/9780203488041Search in Google Scholar

[32] B. Szabó and I. Babuška, Finite Element Analysis, John Wiley & Sons, New York, 1991. Search in Google Scholar

[33] J. Xu, L. Chen and R. H. Nochetto, Optimal multilevel methods for H ( grad ) , H ( curl ) , and H ( div ) systems on graded and unstructured grids, Multiscale, Nonlinear and Adaptive Approximation, Springer, Berlin (2009), 599–659. 10.1007/978-3-642-03413-8_14Search in Google Scholar

[34] X. Zhang, Multilevel Schwarz methods, Numer. Math. 63 (1992), no. 4, 521–539. 10.1007/BF01385873Search in Google Scholar

Received: 2020-02-28
Revised: 2020-09-04
Accepted: 2020-12-16
Published Online: 2021-02-05
Published in Print: 2021-04-01

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 2.5.2024 from https://www.degruyter.com/document/doi/10.1515/cmam-2020-0024/html
Scroll to top button