Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks
<|reference_start|>Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks: The presence of non-convexity in smooth optimization problems arising from deep learning have sparked new smoothness conditions in the literature and corresponding convergence analyses. We discuss these smoothness conditions, order them, provide conditions for determining whether they hold, and evaluate their applicability to training a deep linear neural network for binary classification.<|reference_end|>
@article{patel2024recent,
    title={Recent Advances in Non-convex Smoothness Conditions and Applicability to
  Deep Linear Neural Networks},
    author={Vivak Patel, Christian Varner},
    journal={arXiv preprint arXiv:2409.13672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13672},
    primaryClass={cs.LG math.OC}
}