Zero-stability is the basic property of numerical methods of ordinary differential equations (ODEs). Little work on zero-stability is obtained for the waveform relaxation (WR) methods, although it is an important numerical method of ODEs. In this paper we present a definition of zero-stability of WR methods and prove that several classes of WR methods are zero-stable under the Lipschitz conditions. Also, some numerical examples are given to outline the effectiveness of the developed results.
Citation: Zhencheng Fan. Zero-stability of waveform relaxation methods for ordinary differential equations[J]. Electronic Research Archive, 2022, 30(3): 1126-1141. doi: 10.3934/era.2022060
Zero-stability is the basic property of numerical methods of ordinary differential equations (ODEs). Little work on zero-stability is obtained for the waveform relaxation (WR) methods, although it is an important numerical method of ODEs. In this paper we present a definition of zero-stability of WR methods and prove that several classes of WR methods are zero-stable under the Lipschitz conditions. Also, some numerical examples are given to outline the effectiveness of the developed results.
[1] |
E. Lelarasmee, A. E. Ruehli, L. Sangievanni-Vincentelli, The waveform relaxation method for time-domain analysis of large scale integragted circuits, IEEE Trans. CAD IC Syst., 1 (1982), 131–145. https://doi.org/10.1109/TCAD.1982.1270004 doi: 10.1109/TCAD.1982.1270004
![]() |
[2] |
A. Bellen, Z. Jackiewicz, M. Zennaro, Contractivity of waveform relaxation Runge-Kutta iterations and related limit methods for dissipative systems in the maximum norm, SIAM J. Numer. Anal., 31 (1994), 499–523. https://doi.org/10.1137/0731027 doi: 10.1137/0731027
![]() |
[3] |
C. Dajana, D. Raffaele, P. Beatrice, GPU-acceleration of waveform relaxation methods for large differential systems, Numer. Algorithms, 71 (2016), 293–310. https://doi.org/10.1007/s11075-015-9993-6 doi: 10.1007/s11075-015-9993-6
![]() |
[4] |
Z. C. Fan, Convergence of discrete time waveform relaxation methods, Numer. Algor., 80 (2019), 469–483. https://doi.org/10.1007/s11075-018-0493-3 doi: 10.1007/s11075-018-0493-3
![]() |
[5] |
Z. Hassanzadeh, D. K. Salkuyeh, Two-stage waveform relaxation method for the initial value problems with non-constant coefficients, Comput. Appl. Math., 33 (2014), 641–654. https://doi.org/10.1007/s40314-013-0086-7 doi: 10.1007/s40314-013-0086-7
![]() |
[6] |
K. J. in't Hout, On the convergence of waveform relaxation methods for stiff nolinear ordinary differential equations, Appl. Numer. Math., 18 (1995), 175–190. https://doi.org/10.1016/0168-9274(95)00052-V doi: 10.1016/0168-9274(95)00052-V
![]() |
[7] |
J. Janssen, S. Vandewalle, On SOR waveform relaxation methods, SIAM J. Numer. Anal., 34 (1997), 2456–2481. https://doi.org/10.1137/S0036142995294292 doi: 10.1137/S0036142995294292
![]() |
[8] |
Y. L. Jiang, Windowing waveform relaxation of initial value problems, Acta Math. Appl. Sin., 22 (2006), 575–588. https://doi.org/10.1007/s10255-006-0331-6 doi: 10.1007/s10255-006-0331-6
![]() |
[9] |
J. Sand, K. Burrage, A Jacobi waveform relaxation method for ODEs, SIAM J. Sci. Comput., 20 (1998), 534–552. https://doi.org/10.1137/S1064827596306562 doi: 10.1137/S1064827596306562
![]() |
[10] | J. D. Lambert, Numerical Methods for Ordinary Differential Systems, John Wiley & Sons, Ltd., Chichester, 1991. |
[11] |
K. Burrage, Z. Jackiewicz, S. P. Norsett, R. A. Renaut, Preconditioning waveform relaxation iterations for differential systems, BIT Numer. Math., 36 (1996), 54–76. https://doi.org/10.1007/BF01740544 doi: 10.1007/BF01740544
![]() |
[12] | E. Blåsten, H. Liu, Recovering piecewise constant refractive indices by a single far-field pattern, Inverse Probl., 36 (2020). https://doi.org/10.1088/1361-6420/ab958f |
[13] |
Y. T. Chow, Y. Deng, Y. He, H. Liu, X. Wang, Surface-localized transmission eigenstates, super-resolution imaging, and pseudo surface plasmon modes, SIAM J. Imaging Sci., 14 (2021), 946–975. https://doi.org/10.1137/20M1388498 doi: 10.1137/20M1388498
![]() |
[14] |
W. Yin, W. Yang, H. Liu, A neural network scheme for recovering scattering obstacles with limited phaseless far-field data, J. Comput. Phys., 417 (2020), 109594. https://doi.org/10.1016/j.jcp.2020.109594 doi: 10.1016/j.jcp.2020.109594
![]() |