Symplectic Runge-Kutta schemes for adjoint equations, automatic differentiation, optimal control and more
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Abstract (arXiv)
It is well known that symplectic Runge-Kutta and Partitioned Runge-Kutta methods exactly preserve em quadratic first integrals (invariants of motion) of the system being integrated. While this property is often seen as a mere curiosity (it does not hold for arbitrary first integrals), it plays an important role in the computation of numerical sensitivities, optimal control theory and Lagrangian mechanics, as described in this paper, which, together with some new material, presents in a unified way a number of results now scattered or implicit in the literature. Some widely used procedures, such as the direct method in optimal control theory and the computation of sensitivities via reverse accumulation imply "hidden" integrations with symplectic Partitioned Runge-Kutta schemes.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2015.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1.
4 papers.
- 2021Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memorycited by 11
- 2023Persistent learning signals and working memory without continuous attractorscited by 6
- 2021Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham Systemcited by 1
- 2021Classification with Runge-Kutta networks and feature space augmentationcited by 0
- 2023Persistent learning signals and working memory without continuous attractors24 Aug 2023
- 2021Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System14 Dec 2021
- 2021Classification with Runge-Kutta networks and feature space augmentation6 Apr 2021
- 2021Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory19 Feb 2021