Second-order robust parallel integrators for dynamical low-rank approximation
<|reference_start|>Second-order robust parallel integrators for dynamical low-rank approximation: Due to its reduced memory and computational demands, dynamical low-rank approximation (DLRA) has sparked significant interest in multiple research communities. A central challenge in DLRA is the development of time integrators that are robust to the curvature of the manifold of low-rank matrices. Recently, a parallel robust time integrator that permits dynamic rank adaptation and enables a fully parallel update of all low-rank factors was introduced. Despite its favorable computational efficiency, the construction as a first-order approximation to the augmented basis-update & Galerkin integrator restricts the parallel integrator's accuracy to order one. In this work, an extension to higher order is proposed by a careful basis augmentation before solving the matrix differential equations of the factorized solution. A robust error bound with an improved dependence on normal components of the vector field together with a norm preservation property up to small terms is derived. These analytic results are complemented and demonstrated through a series of numerical experiments.<|reference_end|>
@article{kusch2024second-order,
    title={Second-order robust parallel integrators for dynamical low-rank
  approximation},
    author={Jonas Kusch},
    journal={arXiv preprint arXiv:2403.02834},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02834},
    primaryClass={math.NA cs.NA}
}