Variational symplectic diagonally implicit Runge-Kutta methods for isospectral systems
Clauson Carvalho da Silva, Christian Lessig

TL;DR
This paper develops variational symplectic diagonally implicit Runge-Kutta methods for isospectral systems, ensuring eigenvalue conservation and energy preservation, with practical implementation advantages demonstrated through numerical experiments.
Contribution
It introduces a Lagrangian formulation of isospectral Runge-Kutta methods via discrete Euler-Poincare reduction, generalizing previous Lie group integrators and enabling higher order methods.
Findings
Eigenvalues are conserved with high accuracy.
Energy conservation is achieved in numerical experiments.
Methods are straightforward to implement for higher order accuracy.
Abstract
Isospectral flows appear in a variety of applications, e.g. the Toda lattice in solid state physics or in discrete models for two-dimensional hydrodynamics, with the isospectral property often corresponding to mathematically or physically important conservation laws. Their most prominent feature, i.e. the conservation of the eigenvalues of the matrix state variable, should therefore be retained when discretizing these systems. Recently, it was shown how isospectral Runge-Kutta methods can, in the Lie-Poisson case also considered in our work, be obtained through Hamiltonian reduction of symplectic Runge-Kutta methods on the cotangent bundle of a Lie group. We provide the Lagrangian analogue and, in the case of symplectic diagonal implicit Runge-Kutta methods, derive the methods through a discrete Euler-Poincare reduction. Our derivation relies on a formulation of diagonally implicit…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical methods for differential equations · Advanced Numerical Methods in Computational Mathematics · Differential Equations and Numerical Methods
