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 Mathematics , 2014, Abstract: The properties of the Bigeometric or proportional derivative are presented and discussed explicitly. Based on this derivative, the Bigeometric Taylor theorem is worked out. As an application of this calculus, the Bigeometric Runge-Kutta method is derived and is applied to academic examples, with known closed form solutions, and a sample problem from mathematical modelling in biology. The comparison of the results of the Bigeometric Runge-Kutta method with the ordinary Runge-Kutta method shows that the Bigeometric Runge-Kutta method is at least for a particular set of initial value problems superior with respect to accuracy and computation time to the ordinary Runge-Kutta method.
 Mohd Idris Jayes Matematika , 1997, Abstract: In Mohd Idris [4], the geometric mean approach of deriving the numerical solution for the ordinary differential equations is shown. In this paper, we extend the approach to derive the Runge-Kutta geometric mean method of order 2. Numerical results obtained form selected problems show that the Runge-Kutta geometric mean method is very competitive. However, the Runge-Kutta geometric mean is computationally expensive compared to the standard Runge-Kutta method.
 Discrete Dynamics in Nature and Society , 2008, DOI: 10.1155/2008/790619 Abstract: Standard Runge-Kutta methods are explicit, one-step, and generally constant step-size numerical integrators for the solution of initial value problems. Such integration schemes of orders 3, 4, and 5 require 3, 4, and 6 function evaluations per time step of integration, respectively. In this paper, we propose a set of simple, explicit, and constant step-size Accerelated-Runge-Kutta methods that are two-step in nature. For orders 3, 4, and 5, they require only 2, 3, and 5 function evaluations per time step, respectively. Therefore, they are more computationally efficient at achieving the same order of local accuracy. We present here the derivation and optimization of these accelerated integration methods. We include the proof of convergence and stability under certain conditions as well as stability regions for finite step sizes. Several numerical examples are provided to illustrate the accuracy, stability, and efficiency of the proposed methods in comparison with standard Runge-Kutta methods.
 Christian Brouder Physics , 1999, DOI: 10.1007/s100529900235 Abstract: A connection between the algebra of rooted trees used in renormalization theory and Runge-Kutta methods is pointed out. Butcher's group and B-series are shown to provide a suitable framework for renormalizing a toy model of field the ory, following Kreimer's approach. Finally B-series are used to solve a class of non-linear partial differential equations.
 福州大学学报(自然科学版) , 2009, Abstract: 提出了求解一类随机常微分方程(SODEs)的3种Runge-Kutta格式:显式Runge-Kutta格式、半隐式Runge-Kutta格式和隐式Runge-Kutta格式.讨论了这3种Runge-Kutta格式的T稳定条件,并给出了部分数值实验结果.
 Computer Science , 2014, Abstract: Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state of the art. Like most numerical methods, they return point estimates. We construct a family of probabilistic numerical methods that instead return a Gauss-Markov process defining a probability distribution over the ODE solution. In contrast to prior work, we construct this family such that posterior means match the outputs of the Runge-Kutta family exactly, thus inheriting their proven good properties. Remaining degrees of freedom not identified by the match to Runge-Kutta are chosen such that the posterior probability measure fits the observed structure of the ODE. Our results shed light on the structure of Runge-Kutta solvers from a new direction, provide a richer, probabilistic output, have low computational cost, and raise new research questions.
 Mathematics , 2015, Abstract: Perturbed Runge--Kutta methods (also referred to as downwind Runge--Kutta methods) can guarantee monotonicity preservation under larger step sizes relative to their traditional Runge--Kutta counterparts. In this paper we study, the question of how to optimally perturb a given method in order to increase the radius of absolute monotonicity (a.m.). We prove that for methods with zero radius of a.m., it is always possible to give a perturbation with positive radius. We first study methods for linear problems and then methods for nonlinear problems. In each case, we prove upper bounds on the radius of a.m., and provide algorithms to compute optimal perturbations. We also provide optimal perturbations for many known methods.
 Mathematics , 2015, Abstract: It is a classical theorem of Liouville that Hamiltonian systems preserve volume in phase space. Any symplectic Runge-Kutta method will respect this property for such systems, but it has been shown that no B-Series method can be volume preserving for all volume preserving vector fields (BIT 47 (2007) 351-378 and IMA J. Numer. Anal. 27 (2007) 381-405). In this paper we show that despite this result, symplectic Runge-Kutta methods can be volume preserving for a much larger class of vector fields than Hamiltonian systems, and discuss how some Runge-Kutta methods can preserve a modified measure exactly.
 Tamkang Journal of Mathematics , 2008, DOI: 10.5556/j.tkjm.39.2008.199-211 Abstract: We derive a fth order ve-stage explicit Runge-Kutta composition method, and an error estimator using linear combination of stage values and output values over two steps. Numerical results presented by testing the new pair over DETEST problems show a signi cant improvement.
 Discrete Dynamics in Nature and Society , 2004, DOI: 10.1155/s1026022604311039 Abstract: A simple accelerated third-order Runge-Kutta-type, fixed time step, integration scheme that uses just two function evaluations per step is developed. Because of the lower number of function evaluations, the scheme proposed herein has a lower computational cost than the standard third-order Runge-Kutta scheme while maintaining the same order of local accuracy. Numerical examples illustrating the computational efficiency and accuracy are presented and the actual speedup when the accelerated algorithm is implemented is also provided.
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