A family of parallel Runge-Kutta pairs

AbstractIncreasing availability of parallel computers has recently spurred a substantial amount of research concerned with designing explicit Runge-Kutta methods to be implemented on such computers. Here, we discuss a family of methods that require fewer processors than methods presently available do, still achieving a similar speed-up. In particular, (5,6) and (6,7) pairs are derived, that require a minimum number of function evaluations on two and three processors, respectively

Approximate solution of ordinary differential equations and their systems through discrete and continuous embedded Runge-Kutta formulae and upgrading of their order

AbstractThe eight main contributions of the author to the field of approximate solutions of ordinary differential equations described herein are all application-oriented, with the purposes of simplification and the increase in efficiency and effectiveness of the Runge-Kutta processes generated. They range from the determination of an initial trial step-size to be adopted to expedite the approximation process through embedded Runge-Kutta algorithms to a more recent procedure for upgrading the order of Runge-Kutta processes. These contributions encompass all classes of differential equations of all orders, such as explicit, implicit, single or systems, and their treatment by Runge-Kutta processes of scalar or vector type (with the related equivalence conditions), of discrete or continuous kind, including the computer derivations of nonlinear algebraic equations associated with the Runge-Kutta processes. Specifically, the author developed the first fifth order Runge-Kutta formulae with fourth order embedded and the first C1 approximate solution through interpolation and Runge-Kutta formulae, which he improved by developing C1 embeddings with Runge-Kutta formulae without the use of interpolative techniques

DENSERKS: Fortran sensitivity solvers using continuous, explicit Runge-Kutta schemes

DENSERKS is a Fortran sensitivity equation solver package designed for integrating models whose evolution can be described by ordinary differential equations (ODEs). A salient feature of DENSERKS is its support for both forward and adjoint sensitivity analyses, with built-in integrators for both first and second order continuous adjoint models. The software implements explicit Runge-Kutta methods with adaptive timestepping and high-order dense output schemes for the forward and the tangent linear model trajectory interpolation. Implementations of six Runge-Kutta methods are provided, with orders of accuracy ranging from two to eight. This makes DENSERKS suitable for a wide range of practical applications. The use of dense output, a novel approach in adjoint sensitivity analysis solvers, allows for a high-order cost-effective interpolation. This is a necessary feature when solving adjoints of nonlinear systems using highly accurate Runge-Kutta methods (order five and above). To minimize memory requirements and make long-time integrations computationally efficient, DENSERKS implements a two-level checkpointing mechanism. The code is tested on a selection of problems illustrating first and second order sensitivity analysis with respect to initial model conditions. The resulting derivative information is also used in a gradient-based optimization algorithm to minimize cost functionals dependent on a given set of model parameters

High Order Multistep Methods with Improved Phase-Lag Characteristics for the Integration of the Schr\"odinger Equation

In this work we introduce a new family of twelve-step linear multistep methods for the integration of the Schr\"odinger equation. The new methods are constructed by adopting a new methodology which improves the phase lag characteristics by vanishing both the phase lag function and its first derivatives at a specific frequency. This results in decreasing the sensitivity of the integration method on the estimated frequency of the problem. The efficiency of the new family of methods is proved via error analysis and numerical applications.Comment: 36 pages, 6 figure

Modeling still matters: a surprising instance of catastrophic floating point errors in mathematical biology and numerical methods for ODEs

We guide the reader on a journey through mathematical modeling and numerical analysis, emphasizing the crucial interplay of both disciplines. Targeting undergraduate students with basic knowledge in dynamical systems and numerical methods for ordinary differential equations, we explore a model from mathematical biology where numerical methods fail badly due to catastrophic floating point errors. We analyze the reasons for this behavior by studying the steady states of the model and use the theory of invariants to develop an alternative model that is suited for numerical simulations. Our story intends to motivate combining analytical and numerical knowledge, even in cases where the world looks fine at first sight. We have set up an online repository containing an interactive notebook with all numerical experiments to make this study fully reproducible and useful for classroom teaching.Comment: 17 pages, 10 figure