Mark Sofroniou

Derivation of symmetric composition constants for symmetric integrators

This work focuses on the derivation of composition methods for the numerical integration of ordinary differential equations, which give rise to very challenging optimization problems. Composition is a useful technique for constructing high order approximations, while conserving certain geometric properties. We survey existing composition methods and describe results of an intensive numerical search for new methods. Details of the search procedure are given along with numerical examples, which indicate that the new methods perform better than previously known methods. Some insight into the location of global minima for these problems is obtained as a result.

Precise numerical computation

Abstract Arithmetic systems such as those based on IEEE standards currently make no attempt to track the propagation of errors. A formal error analysis, however, can be complicated and is often confined to the realm of experts in numerical analysis. In recent years, there has been a resurgence of interest in automated methods for accurately monitoring the error propagation. In this article, a floating-point system based on significance arithmetic will be described. Details of the implementation in Mathematica will be given along with examples that illustrate the design goals and differences over conventional fixed-precision floating-point systems.

Symbolic derivation of Runge-Kutta methods

Abstract Computer algebra provides a powerful tool for research in mathematics and the applied sciences. A symbolic package which may be used to explore and derive Runge-Kutta methods is presented. The package is the first of its kind to make use of the elegant graph-theoretical formalism attributed to Butcher.

Order stars and linear stability theory

Abstract Order stars are a powerful modern tool for the development and analysis of numerical methods. They convey important information such as order and stability in a unified framework. A package for rendering order stars becomes part of the standard distribution in the next major release of Mathematica . An introduction to the theory is provided here, set in the context of numerical methods for Ordinary Differential Equations. The implementation is discussed and examples are given to illustrate why a computer algebra system is an ideal environment for the exploration of order stars.

Runge-Kutta methods for quadratic ordinary differential equations

Many systems of ordinary differential equations are quadratic: the derivative can be expressed as a quadratic function of the dependent variable. We demonstrate that this feature can be exploited in the numerical solution by Runge-Kutta methods, since the quadratic structure serves to decrease the number of order conditions. We discuss issues related to construction design and implementation and present a number of new methods of Runge-Kutta and Runge-Kutta-Nyström type that display superior behaviour when applied to quadratic ordinary differential equations.

Symplectic Methods for Separable Hamiltonian Systems

This paper focuses on the solution of separable Hamiltonian systems using explicit symplectic integration methods. Strategies for reducing the effect of cumulative rounding errors are outlined and advantages over a standard formulation are demonstrated. Procedures for automatically choosing appropriate methods are also described.

Increment formulations for rounding error reduction in the numerical solution of structured differential systems

Strategies for reducing the effect of cumulative rounding errors in geometric numerical integration are outlined. The focus is, in particular, on the solution of separable Hamiltonian systems using explicit symplectic integration methods and on solving orthogonal matrix differential systems using projection. Examples are given that demonstrate the advantages of an increment formulation over the standard implementation of conventional integrators. We describe how the aforementioned special purpose integration methods have been set up in a uniform, modular and extensible framework being developed in the problem solving environment Mathematica.

Solving Orthogonal Matrix Differential Systems in Mathematica

A component of a new environment for the numerical solution of ordinary differential equations in Mathematica is outlined. We briefly describe how special purpose integration methods can be constructed to solve structured dynamical systems. In particular we focus on the solution of orthogonal matrix differential systems using projection. Examples are given to illustrate the advantages of a projection scheme over conventional integration methods.

On the construction of a new generalization of Runge-Kutta methods

Abstract We give an overview of the construction of algebraic conditions for determining the order of Runge-Kutta methods and describe a novel extension for numerically solving systems of differential equations. The new schemes, called Elementary Differential Runge-Kutta methods, include as a subset Runge-Kutta methods, Taylor series methods, Multiderivative Runge-Kutta methods. We outline how order conditions have been constructed for the new schemes using B-series and their composition and give details relating to a Mathematica implementation.