Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning

@InProceedings{pmlr-v48-lid16,
  title = 	 {Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning},
  author = 	 {Li, Xingguo and Zhao, Tuo and Arora, Raman and Liu, Han and Haupt, Jarvis},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {917--925},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/lid16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/lid16.html},
  abstract = 	 {We propose a stochastic variance reduced optimization algorithm for solving a class of large-scale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.}
}