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{Projected Newton-type methods in machine learning}


Book Chapter


{We consider projected Newton-type methods for solving large-scale optimization problems arising in machine learning and related fields. We first introduce an algorithmic framework for projected Newton-type methods by reviewing a canonical projected (quasi-)Newton method. This method, while conceptually pleasing, has a high computation cost per iteration. Thus, we discuss two variants that are more scalable, namely, two-metric projection and inexact projection methods. Finally, we show how to apply the Newton-type framework to handle non-smooth objectives. Examples are provided throughout the chapter to illustrate machine learning applications of our framework.}

Author(s): Schmidt, M. and Kim, D. and Sra, S.
Book Title: Optimization for Machine Learning
Pages: 305--330
Year: 2011
Publisher: MIT Press

Department(s): Modern Magnetic Systems
Bibtex Type: Book Chapter (incollection)

Address: Cambridge, MA, USA
URL: http://www.kyb.tuebingen.mpg.de//fileadmin/user\textunderscoreupload/files/publications/2011\textunderscoreOPT\textunderscoreChapter\textunderscore6824[0].pdf


  title = {{Projected Newton-type methods in machine learning}},
  author = {Schmidt, M. and Kim, D. and Sra, S.},
  booktitle = {{Optimization for Machine Learning}},
  pages = {305--330},
  publisher = {MIT Press},
  address = {Cambridge, MA, USA},
  year = {2011},
  url = {http://www.kyb.tuebingen.mpg.de//fileadmin/user\textunderscoreupload/files/publications/2011\textunderscoreOPT\textunderscoreChapter\textunderscore6824[0].pdf}