Natural continuous extensions of Runge-Kutta formulas
Mathematics of Computation
The numerical analysis of ordinary differential equations: Runge-Kutta and general linear methods
The numerical analysis of ordinary differential equations: Runge-Kutta and general linear methods
SIAM Journal on Numerical Analysis
Derivation of efficient, continuous, explicit Runge-Kutta methods
SIAM Journal on Scientific and Statistical Computing
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
The tolerance proportionality of adaptive ODE solvers
Journal of Computational and Applied Mathematics - Special issue on numerical methods for ordinary differential equations
A general class of two-step Runge-Kutta methods for ordinary differential equations
SIAM Journal on Numerical Analysis
Implementation of two-step Runge-Kutta methods for ordinary differential equations
Journal of Computational and Applied Mathematics
SIAM Journal on Scientific Computing
Order conditions for two-step Runge-Kutta methods
Selected papers of the second international conference on Numerical solution of Volterra and delay equations : Volterra centennial: Volterra centennial
Order Conditions for General Two-Step Runge--Kutta Methods
SIAM Journal on Numerical Analysis
SIAM Journal on Numerical Analysis
Pseudo-Runge-Kutta Methods Involving Two Points
Journal of the ACM (JACM)
Construction of two-step Runge--Kutta methods with large regions of absolute stability
Journal of Computational and Applied Mathematics
Two-step Runge-Kutta Methods with Quadratic Stability Functions
Journal of Scientific Computing
Mathematics and Computers in Simulation
Explicit Nordsieck methods with quadratic stability
Numerical Algorithms
Search for efficient general linear methods for ordinary differential equations
Journal of Computational and Applied Mathematics
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We describe a new representation of explicit two-step Runge-Kutta methods for ordinary differential equations. This representation makes it possible for the accurate and reliable estimation of local discretization error and facilitates the efficient implementation of these methods in a variable stepsize environment.