Corpus ID: 203951320

Bregman Proximal Framework for Deep Linear Neural Networks

@article{Mukkamala2019BregmanPF,
  title={Bregman Proximal Framework for Deep Linear Neural Networks},
  author={Mahesh Chandra Mukkamala and Felix Westerkamp and E. Laude and D. Cremers and P. Ochs},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.03638}
}

Mahesh Chandra Mukkamala, Felix Westerkamp, +2 authors P. Ochs

Published 2019

Computer Science, Mathematics

ArXiv

A typical assumption for the analysis of first order optimization methods is the Lipschitz continuity of the gradient of the objective function. However, for many practical applications this assumption is violated, including loss functions in deep learning. To overcome this issue, certain extensions based on generalized proximity measures known as Bregman distances were introduced. This initiated the development of the Bregman proximal gradient (BPG) algorithm and an inertial variant (momentum… CONTINUE READING

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