Cubic Regularization with Momentum for Nonconvex Optimization

@inproceedings{Wang2018CubicRW,
  title={Cubic Regularization with Momentum for Nonconvex Optimization},
  author={Zhenchang Wang and Yi Zhou and Yingbin Liang and Guanghui Lan},
  booktitle={UAI},
  year={2018}
}
Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. However, such a successful acceleration technique has not yet been proposed for second-order algorithms in nonconvex optimization.In this paper, we apply the momentum scheme to cubic regularized (CR) Newton's method and explore the potential for acceleration. Our numerical experiments on various nonconvex optimization… CONTINUE READING
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