Abstract
The traditional derivation of Runge–Kutta methods is based on the use of the scalar test problem y′(x)=f(x,y(x)). However, above order 4, this gives less restrictive order conditions than those obtained from a vector test problem using a tree-based theory. In this paper, stumps, or incomplete trees, are introduced to explain the discrepancy between the two alternative theories. Atomic stumps can be combined multiplicatively to generate all trees. For the scalar test problem, these quantities commute, and certain sets of trees form isomeric classes. There is a single order condition for each class, whereas for the general vector-based problem, for which commutation of atomic stumps does not occur, there is exactly one order condition for each tree. In the case of order 5, the only nontrivial isomeric class contains two trees, and the number of order conditions reduces from 17 to 16 for scalar problems. A method is derived that satisfies the 16 conditions for scalar problems but not the complete set based on 17 trees. Hence, as a practical numerical method, it has order 4 for a general initial value problem, but this increases to order 5 for a scalar problem.
Highlights
Trees have a well-established role in the analysis of numerical methods for ordinary differential equations
The more general concept of a stump is introduced and applied to the analysis of B-series and the composition rule. It is shown how stumps can be used to analyse the order of nonautonomous scalar problems for which the order conditions for Runge–Kutta methods are slightly different
These are a generalisation of trees, but, by restricting to “atomic stumps”, they provide a means of generating all trees
Summary
Trees have a well-established role in the analysis of numerical methods for ordinary differential equations. The more general concept of a stump is introduced and applied to the analysis of B-series and the composition rule It is shown how stumps can be used to analyse the order of nonautonomous scalar problems for which the order conditions for Runge–Kutta methods are slightly different.
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