University of Cambridge > Talks.cam > Cambridge Analysts' Knowledge Exchange (C.A.K.E.) > Discrete gradient methods for solving nonconvex, nonsmooth optimisation problems in image analysis

Discrete gradient methods for solving nonconvex, nonsmooth optimisation problems in image analysis

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

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

We will give an introduction to the discrete gradient method, which is a novel optimisation technique for solving convex and nonconvex variational problems in image processing. Using discretisation tools from geometric numerical integration, these methods are designed to preserve the dissipation of gradient flow systems in a uniformly stable manner. In this talk, we will discuss how discrete gradient methods connects to, and compares to, other methods in gradient-based, as well as derivative-free, optimisation. Particular emphasis will be on nonsmooth, nonconvex optimisation analysis, using the Clarke subdifferential. We will also motivate the results with examples from image analysis.

This talk is part of the Cambridge Analysts' Knowledge Exchange (C.A.K.E.) series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity