The oracle Complexity of Smooth Convex Optimization in Nonstandard Settings

author: Cristóbal Guzmán, University of Chile
published: Aug. 20, 2015,   recorded: July 2015,   views: 1573


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


First-order convex minimization algorithms are currently the methods of choice for large-scale sparse – and more generally parsimonious – regression models. We pose the question on the limits of performance of black-box oriented methods for convex minimization in non-standard settings, where the regularity of the objective is measured in a norm not necessarily induced by the feasible domain. This question is studied for `p/`q-settings, and their matrix analogues (Schatten norms), where we find surprising gaps on lower bounds compared to state of the art methods. We propose a conjecture on the optimal convergence rates for these settings, for which a positive answer would lead to significant improvements on minimization algorithms for parsimonious regression models.

See Also:

Download slides icon Download slides: colt2015_guzman_nonstandard_settings_01.pdf (74.7 KB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: