University of Cambridge > Talks.cam > Applied and Computational Analysis > Low-rank optimization: from differential geometry to recommender systems

Low-rank optimization: from differential geometry to recommender systems

Add to your list(s) Download to your calendar using vCal

  • UserPierre-Antoine Absil (Louvain)
  • ClockThursday 30 January 2014, 15:00-16:00
  • HouseMR 14, CMS.

If you have a question about this talk, please contact ai10.

The central topic of this talk is low-rank optimization, where the archetypal problem consists of minimizing a real-valued function defined on a set of matrices of fixed or bounded rank. The fact that the set of fixed-rank matrices admits Riemannian manifold structures endows the problem with rich geometry. We will see how geometric concepts can be exploited to design efficient low-rank optimization methods, and we will show how low-rank optimization applies to recommender systems. This talk is based on joint work with Nicolas Boumal.

This talk is part of the Applied and Computational Analysis series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity