Corpus ID: 196831612

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

@article{Adil2019FastPC,
  title={Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression},
  author={Deeksha Adil and Richard Peng and Sushant Sachdeva},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.07167}
}
  • Deeksha Adil, Richard Peng, Sushant Sachdeva
  • Published in NeurIPS 2019
  • Computer Science, Mathematics
  • ArXiv
  • Linear regression in $\ell_p$-norm is a canonical optimization problem that arises in several applications, including sparse recovery, semi-supervised learning, and signal processing. Generic convex optimization algorithms for solving $\ell_p$-regression are slow in practice. Iteratively Reweighted Least Squares (IRLS) is an easy to implement family of algorithms for solving these problems that has been studied for over 50 years. However, these algorithms often diverge for p > 3, and since the… CONTINUE READING

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.