• Corpus ID: 195791848

Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses

@inproceedings{MarteauFerey2019GloballyCN,
  title={Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses},
  author={Ulysse Marteau-Ferey and Francis R. Bach and Alessandro Rudi},
  booktitle={Neural Information Processing Systems},
  year={2019}
}
In this paper, we study large-scale convex optimization algorithms based on the Newton method applied to regularized generalized self-concordant losses, which include logistic regression and softmax regression. We first prove that our new simple scheme based on a sequence of problems with decreasing regularization parameters is provably globally convergent, that this convergence is linear with a constant factor which scales only logarithmically with the condition number. In the parametric… 

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