Linearly implicit Runge-Kutta methods and approximate matrix factorization

Linearly implicit Runge-Kutta methods are a class of suitable time integrators for initial value problems of ordinary differential systems whose right-hand side function can be written as the sum of a stiff linear part and a nonlinear term. Such systems arise for instance after spatial discretization of taxis-diffusion-reaction systems from mathematical biology. When approximate matrix factorization is used for efficiently solving the stage equations appearing in these methods, then the order of the methods is reduced to one. In this paper we analyse this fact and propose an appropriate and efficient correction to achieve order two while preserving the main stability properties of the underlying method. Numerical experiments with LIRK3 [Appl. Numer. Math. 37 (2001) 535] illustrating the theory are provided. In the case of taxis-diffusion-reaction systems, the corrected method compares well with other suitable schemes.

[1]  Jorge Bardina,et al.  Three dimensional hypersonic flow simulations with the CSCM implicit upwind Navier-Stokes method. [Conservative Supra-Characteristic Method] , 1987 .

[2]  M. Chaplain,et al.  A model mechanism for the chemotactic response of endothelial cells to tumour angiogenesis factor. , 1993, IMA journal of mathematics applied in medicine and biology.

[3]  M. Chaplain,et al.  Continuous and discrete mathematical models of tumor-induced angiogenesis , 1998, Bulletin of mathematical biology.

[4]  M. Chaplain,et al.  Mathematical modelling of tumour invasion and metastasis , 2000 .

[5]  Robert W. MacCormack,et al.  A NEW IMPLICIT ALGORITHM FOR FLUID FLOW , 1997 .

[6]  M. Calvo,et al.  Linearly implicit Runge—Kutta methods for advection—reaction—diffusion equations , 2001 .

[7]  L. Shampine,et al.  Numerical Solution of Ordinary Differential Equations. , 1995 .

[8]  Iterative modified approximate factorization , 2001 .

[9]  E. Hairer,et al.  Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems , 1993 .

[10]  Ben P. Sommeijer,et al.  Approximate factorization for time-dependent partial differential equations , 1999 .

[11]  Willem Hundsdorfer,et al.  A Second-Order Rosenbrock Method Applied to Photochemical Dispersion Problems , 1999, SIAM J. Sci. Comput..

[12]  F. Krogh,et al.  Solving Ordinary Differential Equations , 2019, Programming for Computations - Python.

[13]  H. L. Stone ITERATIVE SOLUTION OF IMPLICIT APPROXIMATIONS OF MULTIDIMENSIONAL PARTIAL DIFFERENTIAL EQUATIONS , 1968 .

[14]  Steven J. Ruuth,et al.  Implicit-explicit Runge-Kutta methods for time-dependent partial differential equations , 1997 .

[15]  J. Verwer,et al.  Numerical solution of time-dependent advection-diffusion-reaction equations , 2003 .

[16]  E. Hairer,et al.  Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems , 2010 .

[17]  Alf Gerisch,et al.  Operator splitting and approximate factorization for taxis-diffusion-reaction models , 2002 .

[18]  Rüdiger Weiner,et al.  The positivity of low-order explicit Runge-Kutta schemes applied in splitting methods , 2003 .