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Splitting and composition methods in the numerical integration of differential equations

1 Pith paper cite this work, alongside 83 external citations. Polarity classification is still indexing.

1 Pith paper citing it
83 external citations · Pith
abstract

We provide a comprehensive survey of splitting and composition methods for the numerical integration of ordinary differential equations (ODEs). Splitting methods constitute an appropriate choice when the vector field associated with the ODE can be decomposed into several pieces and each of them is integrable. This class of integrators are explicit, simple to implement and preserve structural properties of the system. In consequence, they are specially useful in geometric numerical integration. In addition, the numerical solution obtained by splitting schemes can be seen as the exact solution to a perturbed system of ODEs possessing the same geometric properties as the original system. This backward error interpretation has direct implications for the qualitative behavior of the numerical solution as well as for the error propagation along time. Closely connected with splitting integrators are composition methods. We analyze the order conditions required by a method to achieve a given order and summarize the different families of schemes one can find in the literature. Finally, we illustrate the main features of splitting and composition methods on several numerical examples arising from applications.

fields

stat.CO 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Adaptive tuning of Hamiltonian Monte Carlo methods

stat.CO · 2025-06-04 · conditional · novelty 5.0

ATune combines Gaussian theoretical analysis with burn-in simulation data to select system-specific splitting integrators and hyperparameter credible intervals for improved HMC stability and performance.

citing papers explorer

Showing 1 of 1 citing paper.

  • Adaptive tuning of Hamiltonian Monte Carlo methods stat.CO · 2025-06-04 · conditional · none · ref 44 · internal anchor

    ATune combines Gaussian theoretical analysis with burn-in simulation data to select system-specific splitting integrators and hyperparameter credible intervals for improved HMC stability and performance.