Riemannian conjugate Sobolev gradients and their application to compute ground states of BECs
<|reference_start|>Riemannian conjugate Sobolev gradients and their application to compute ground states of BECs: This work considers the numerical computation of ground states of rotating Bose-Einstein condensates (BECs) which can exhibit a multiscale lattice of quantized vortices. This problem involves the minimization of an energy functional on a Riemannian manifold. For this we apply the framework of nonlinear conjugate gradient methods in combination with the paradigm of Sobolev gradients to investigate different metrics. Here we build on previous work that proposed to enhance the convergence of regular Riemannian gradients methods by an adaptively changing metric that is based on the current energy. In this work, we extend this approach to the branch of Riemannian conjugate gradient (CG) methods and investigate the arising schemes numerically. Special attention is given to the selection of the momentum parameter in search direction and how this affects the performance of the resulting schemes. As known from similar applications, we find that the choice of the momentum parameter plays a critical role, with certain parameters reducing the number of iterations required to achieve a specified tolerance by a significant factor. Besides the influence of the momentum parameters, we also investigate how the methods with adaptive metric compare to the corresponding realizations with a standard $H^1_0$-metric. As one of our main findings, the results of the numerical experiments show that the Riemannian CG method with the proposed adaptive metric along with a Polak-Ribi\'ere or Hestenes-Stiefel-type momentum parameter show the best performance and highest robustness compared to the other CG methods that were part of our numerical study.<|reference_end|>
@article{ai2024riemannian,
    title={Riemannian conjugate Sobolev gradients and their application to compute
  ground states of BECs},
    author={Yueshan Ai, Patrick Henning, Mahima Yadav, Sitong Yuan},
    journal={arXiv preprint arXiv:2409.17302},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17302},
    primaryClass={math.NA cs.NA}
}