doi: 10.3934/jcd.2021030
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Symplectic P-stable additive Runge—Kutta methods

Department of Mathematics, University of Bergen, Postbox 7803, 5020 Bergen, Norway

Received  December 2020 Revised  July 2021 Early access December 2021

Classical symplectic partitioned Runge–Kutta methods can be obtained from a variational formulation where all the terms in the discrete Lagrangian are treated with the same quadrature formula. We construct a family of symplectic methods allowing the use of different quadrature formulas (primary and secondary) for different terms of the Lagrangian. In particular, we study a family of methods using Lobatto quadrature (with corresponding Lobatto IIIA-B symplectic pair) as a primary method and Gauss–Legendre quadrature as a secondary method. The methods have the same implicitness as the underlying Lobatto IIIA-B pair, and, in addition, they are P-stable, therefore suitable for application to highly oscillatory problems.

Citation: Antonella Zanna. Symplectic P-stable additive Runge—Kutta methods. Journal of Computational Dynamics, doi: 10.3934/jcd.2021030
References:
[1]

E. Celledoni and E. H. Høyseth, The averaged Lagrangian method, J. Comput. Appl. Math., 316 (2017), 161-174.  doi: 10.1016/j.cam.2016.09.047.  Google Scholar

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G. J. Cooper and A. Sayfy, Additive Runge–Kutta methods for stiff ordinary differential equations, Math. Comp., 40 (1983), 207-218.  doi: 10.1090/S0025-5718-1983-0679441-1.  Google Scholar

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E. Hairer, C. Lubich and G. Wanner, Geometric Numerical Integration, Structure-Preserving Algorithms for Ordinary Differential Equations, Springer Series in Computational Mathematics, Springer, 2010.  Google Scholar

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L. Jay, Specialized partitioned additive Runge–Kutta methods for systems of overdetermined DAEs with holonomic constraints, SIAM J. Numer. Anal., 45 (2007), 1814-1842.  doi: 10.1137/060667475.  Google Scholar

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L. Jay, Structure preservation for constrained dynamics with super partitioned additive Runge–Kutta methods, SIAM J. Sci. Comput., 20 (1998), 416-446.  doi: 10.1137/S1064827595293223.  Google Scholar

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L. O. Jay and L. R. Petzold, Highly oscillatory systems and periodic stability, Technical Report, Army High Performance Computing Research Center, Stanford, CA, (1995), 95–105. Google Scholar

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J. E. Marsden and M. West, Discrete mechanics and variational integrators, Acta Numerica, 10 (2001), 357-514.  doi: 10.1017/S096249290100006X.  Google Scholar

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R. I. McLachlan and A. Stern, Modified trigonometric integrators, SIAM J. Numer. Anal., 52 (2014), 1378-1397.  doi: 10.1137/130921118.  Google Scholar

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R. I. McLachlanY. Sun and P. S. P. Tse, Linear stability of partitioned Runge-Kutta methods, SIAM J. Numer. Anal., 49 (2011), 232-263.  doi: 10.1137/100787234.  Google Scholar

[10]

F. Pfeil, A Higher Order IMEX Method for Solving Highly Oscillatory Problems, Master's thesis, University of Bergen, Norway, June 2019. Google Scholar

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A. Sandu and M. Günther, A generalized-structure approach to additive Runge–Kutta methods, SIAM J. Numer. Anal., 53 (2015), 17-42.  doi: 10.1137/130943224.  Google Scholar

[12]

A. Stern and E. Grinspun, Implicit-explicit variational integration of highly oscillatory problems, Multiscale Model. Simul., 7 (2009), 1779-1794.  doi: 10.1137/080732936.  Google Scholar

[13]

G. M. Tanner, Generalized Additive Runge–Kutta Methods for Stiff Odes, PhD thesis, University of Iowa, 2018.  Google Scholar

[14]

T. Wenger, S. Ober-Blöbaum and S. Leyendecker, Variational integrators of mixed order for dynamical systems with multiple time scales and split potentials, ECCOMAS Congress 2016, 2016. doi: 10.7712/100016.1920.10163.  Google Scholar

[15]

T. WengerS. Ober-Blöbaum and S. Leyendecker, Construction and analysis of higher order variational integrators for dynamical systems with holonomic constraints, Adv. Comput. Math., 43 (2017), 1163-1195.  doi: 10.1007/s10444-017-9520-5.  Google Scholar

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H. Yoshida, Construction of higher order symplectic integrators, Phys. Lett. A, 150 (1990), 262-268.  doi: 10.1016/0375-9601(90)90092-3.  Google Scholar

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A. Zanna, A family of modified trigonometric integrators for highly oscillatory problems, FoCM, Barcelona, 2017. Google Scholar

[18]

M. Zhang and R. D. Skeel, Cheap implicit symplectic integrators, Appl. Numer. Math., 25 (1997), 297-302.  doi: 10.1016/S0168-9274(97)00066-4.  Google Scholar

show all references

References:
[1]

E. Celledoni and E. H. Høyseth, The averaged Lagrangian method, J. Comput. Appl. Math., 316 (2017), 161-174.  doi: 10.1016/j.cam.2016.09.047.  Google Scholar

[2]

G. J. Cooper and A. Sayfy, Additive Runge–Kutta methods for stiff ordinary differential equations, Math. Comp., 40 (1983), 207-218.  doi: 10.1090/S0025-5718-1983-0679441-1.  Google Scholar

[3]

E. Hairer, C. Lubich and G. Wanner, Geometric Numerical Integration, Structure-Preserving Algorithms for Ordinary Differential Equations, Springer Series in Computational Mathematics, Springer, 2010.  Google Scholar

[4]

L. Jay, Specialized partitioned additive Runge–Kutta methods for systems of overdetermined DAEs with holonomic constraints, SIAM J. Numer. Anal., 45 (2007), 1814-1842.  doi: 10.1137/060667475.  Google Scholar

[5]

L. Jay, Structure preservation for constrained dynamics with super partitioned additive Runge–Kutta methods, SIAM J. Sci. Comput., 20 (1998), 416-446.  doi: 10.1137/S1064827595293223.  Google Scholar

[6]

L. O. Jay and L. R. Petzold, Highly oscillatory systems and periodic stability, Technical Report, Army High Performance Computing Research Center, Stanford, CA, (1995), 95–105. Google Scholar

[7]

J. E. Marsden and M. West, Discrete mechanics and variational integrators, Acta Numerica, 10 (2001), 357-514.  doi: 10.1017/S096249290100006X.  Google Scholar

[8]

R. I. McLachlan and A. Stern, Modified trigonometric integrators, SIAM J. Numer. Anal., 52 (2014), 1378-1397.  doi: 10.1137/130921118.  Google Scholar

[9]

R. I. McLachlanY. Sun and P. S. P. Tse, Linear stability of partitioned Runge-Kutta methods, SIAM J. Numer. Anal., 49 (2011), 232-263.  doi: 10.1137/100787234.  Google Scholar

[10]

F. Pfeil, A Higher Order IMEX Method for Solving Highly Oscillatory Problems, Master's thesis, University of Bergen, Norway, June 2019. Google Scholar

[11]

A. Sandu and M. Günther, A generalized-structure approach to additive Runge–Kutta methods, SIAM J. Numer. Anal., 53 (2015), 17-42.  doi: 10.1137/130943224.  Google Scholar

[12]

A. Stern and E. Grinspun, Implicit-explicit variational integration of highly oscillatory problems, Multiscale Model. Simul., 7 (2009), 1779-1794.  doi: 10.1137/080732936.  Google Scholar

[13]

G. M. Tanner, Generalized Additive Runge–Kutta Methods for Stiff Odes, PhD thesis, University of Iowa, 2018.  Google Scholar

[14]

T. Wenger, S. Ober-Blöbaum and S. Leyendecker, Variational integrators of mixed order for dynamical systems with multiple time scales and split potentials, ECCOMAS Congress 2016, 2016. doi: 10.7712/100016.1920.10163.  Google Scholar

[15]

T. WengerS. Ober-Blöbaum and S. Leyendecker, Construction and analysis of higher order variational integrators for dynamical systems with holonomic constraints, Adv. Comput. Math., 43 (2017), 1163-1195.  doi: 10.1007/s10444-017-9520-5.  Google Scholar

[16]

H. Yoshida, Construction of higher order symplectic integrators, Phys. Lett. A, 150 (1990), 262-268.  doi: 10.1016/0375-9601(90)90092-3.  Google Scholar

[17]

A. Zanna, A family of modified trigonometric integrators for highly oscillatory problems, FoCM, Barcelona, 2017. Google Scholar

[18]

M. Zhang and R. D. Skeel, Cheap implicit symplectic integrators, Appl. Numer. Math., 25 (1997), 297-302.  doi: 10.1016/S0168-9274(97)00066-4.  Google Scholar

Figure 1.  Left: Plot of the stability functions for the the Lobatto IIIA-B and Gauss–Legendre combinations of order two (IMEX), order four and order six with coefficients constructed by interpolation $ (20) $. These methods are P-stable. Right: Stability function plot for the methods with coefficients constructed by collocation $ (21) $. For P-stability, the function must have values between $ -1 $ and $ 1 $ for all $ \mu $. These methods are not P-stable. See text for their interval of stability
Figure 2.  Modified frequency for the methods in the Lobatto IIIA and Gauss—Legendre family of order two (IMEX), four and six. The straight line is the identity function, for which $ \tilde \lambda = \lambda $. The IMEX retains the correct frequency of oscillations up to $ h\lambda\approx 1 $. The order two method retains the correct frequency up to $ h\lambda\approx 2 $, while the order six method up to $ h\lambda \approx 3 $
Figure 3.  Left: Individual oscillatory energies $ I_i $ and total oscillatory energy $ I = \sum_i I_i $. Right: Energy error $ |H-H_0| $. The simulations are performed in $ [0, 200] $ with $ \omega = 50 $ and $ h = 2/\omega = 0.04 $. See text for initial conditions
Figure 4.  Left: Maximum deviation in the Hamiltonian (total) energy error. Right: Maximum deviation in scaled oscillatory energy $ \omega I $ error. The computation is performed in [0, 200] for $ h\omega/\pi \in ( 0, 4.5] $, $ h = 0.02 $. The peaks correspond to the resonances of the methods. These occur when $ \cos(h\tilde \omega) = \pm1 $, namely when $ h\omega/\pi \approx 1.1 $ for the order four method and when $ h\omega/\pi \approx 1, 2.47 $ for the order six method. See text for details
Figure 3, with step size $ h = 0.1 $, $ \omega = 50 $. Right: Same oscillatory energies as in the left plot. Here the step size is kept fixed to $ h = 0.1 $ but the frequency $ \omega $ as well as the length of the interval is scaled by a factor of $ 20 $">Figure 5.  Left: Individual oscillatory energies $ I_i $ and total oscillatory energy $ I = \sum_i I_i $, as in Figure 3, with step size $ h = 0.1 $, $ \omega = 50 $. Right: Same oscillatory energies as in the left plot. Here the step size is kept fixed to $ h = 0.1 $ but the frequency $ \omega $ as well as the length of the interval is scaled by a factor of $ 20 $
Figure 6.  Hamiltonian max error ratio for $ h = 0.04 $ and $ h = 0.02 $, $ T = 200 $. We observe peaks in correspondence of the resonances
Figure 7.  Errors at $ T = 3 $ in the slow positions (left) and slow momenta (right) for the Lobatto-Gauss method of order 4 against the step size $ h $ for $ \omega = 10, \ldots, 10^4 $. The lines for $ h^2 $ and $ h^4 $ are plotted for convenience
Figure 8.  Error in the slow positions (left) and slow momenta (right) for the Lobatto-Gauss method of order 6
Figure 9.  Error in the slow positions (left) and slow momenta (right) for the IMEX method with a Yoshida time stepping for a method of order 4. There is a order two reduction in the momenta, but only an order one reduction for the error in the slow positions
Figure 10.  Error in the slow positions (left) and slow momenta (right) for the IMEX method with a Yoshida time stepping yielding a method of order 6. Also in this case there is an observable reduction in the order. We have four orders loss for the momenta and three order loss in the positions
Figure 11.  Comparison of the error in the slow positions (left) and slow momenta for the interpolation Lobatto—Gauss (solid line) and the IMEX-Yoshida method (dashed line) of order four
Figure 12.  Comparison of the error in the slow positions (left) and slow momenta for the interpolation Lobatto—Gauss (solid line) and the IMEX-Yoshida method (dashed line) of order six
Figure 3, this time using the IMEX and its order four and six implementation using the Yoshida technique. The Hamiltonian error (right) does not separate as clearly as the Lobatto-Gauss methods of corresponding order">Figure 13.  Same parameters and initial conditions as in Figure 3, this time using the IMEX and its order four and six implementation using the Yoshida technique. The Hamiltonian error (right) does not separate as clearly as the Lobatto-Gauss methods of corresponding order
Figure 3, except for the step-size, $ h = 0.005 $. It is known that the Yoshida technique can lead to a large error constant in the leading error term">Figure 14.  Comparison of the Hamiltonian error for the Lobatto-Gauss methods (left) and Yoshida methods of the same order (right), same parameters and initial conditions as in Figure 3, except for the step-size, $ h = 0.005 $. It is known that the Yoshida technique can lead to a large error constant in the leading error term
Figure 4, this time with the IMEX and its order four and six implementations using the Yoshida technique">Figure 15.  Left: Maximum error in Hamiltonian (total) energy. Right: Maximum deviation in scaled oscillatory energy $ \omega I $. Same parameters and initial conditions as in Figure 4, this time with the IMEX and its order four and six implementations using the Yoshida technique
Figure 5, this time with the IMEX and its order four and six implementations using the Yoshida technique">Figure 16.  Same parameters and initial conditions as in Figure 5, this time with the IMEX and its order four and six implementations using the Yoshida technique
Figure 6">Figure 17.  Hamiltonian max error ratio for the IMEX and its higher order implementations with the Yoshida technique. Same initial conditions and parameters as in Figure 6
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