Information-theoretic lower bounds on the oracle complexity of sparse convex optimization

author: Alekh Agarwal, Microsoft Research
published: Jan. 13, 2011,   recorded: December 2010,   views: 4021


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.


Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Recent years have seen a surge in optimization methods tailored to sparse optimization problems. In this paper, we study the complexity of stochastic convex optimization in an oracle model of computation, when the objective is optimized at a sparse vector in a high dimensional space. Our result is matched by an appropriately tuned method of mirror descent, establishing the minimiax optimality of the result.

See Also:

Download slides icon Download slides: nipsworkshops2010_agarwal_itl_01.pdf (307.9┬áKB)

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: