On the Convergence of Warped Proximal Iterations for Solving Nonmonotone Inclusions and Applications

Abstract

In machine learning, tackling fairness, robustness, and safeness requires to solve nonconvex optimization problems with various constraints. In this paper, we investigate the warped proximal iterations for solving the nonmonotone inclusions and its application to nonconvex QP with equality constraints.

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Cite

Text

Papadimitriou and Vu. "On the Convergence of Warped Proximal Iterations for Solving Nonmonotone Inclusions and Applications." NeurIPS 2023 Workshops: OPT, 2023.

Markdown

[Papadimitriou and Vu. "On the Convergence of Warped Proximal Iterations for Solving Nonmonotone Inclusions and Applications." NeurIPS 2023 Workshops: OPT, 2023.](https://mlanthology.org/neuripsw/2023/papadimitriou2023neuripsw-convergence/)

BibTeX

@inproceedings{papadimitriou2023neuripsw-convergence,
  title     = {{On the Convergence of Warped Proximal Iterations for Solving Nonmonotone Inclusions and Applications}},
  author    = {Papadimitriou, Dimitri and Vu, Bang Công},
  booktitle = {NeurIPS 2023 Workshops: OPT},
  year      = {2023},
  url       = {https://mlanthology.org/neuripsw/2023/papadimitriou2023neuripsw-convergence/}
}