Approximate Newton Methods and Their Local Convergence

By Haishan Ye, Luo Luo, Zhihua Zhang. 2017

26 citations recorded by the source corpus.

Subfields: Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

1. Simon Lacoste-Julien
2. Issam Hadj Laradji
3. Sharan Vaswani
4. Nicolas Loizou
5. Peter Richtárik
6. Mark W. Schmidt
7. S. Meng
8. Shusen Wang

Citations in the corpus

• Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation (2019, methodology): “This relationship between the regularization and sample size is consistent with the observations of Ye et al. (2017).”
• A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization (2018, methodology): “A recent discussion of different stochastic second order algorithms can also be found in [97].”
• Do Subsampled Newton Methods Work for High-Dimensional Data? (2019, methodology): “…(SSN) methods for reducing the per-iteration complexity of the Newton’s method (Byrd et al. 2011; Pilanci and Wain-wright 2015; Roosta-Khorasani and Mahoney 2016; Pi-lanci and Wainwright 2017; Xu, Roosta Khorasan, and Ma-honey 2017; Berahas, Bollapragada, and Nocedal 2017; Ye, Luo, and Zhang 2017).”