University of Cambridge > Talks.cam > Microsoft Research Cambridge, public talks > CMA-ES – a Stochastic Second-Order Method for Function-Value FreeNumerical Optimization

CMA-ES – a Stochastic Second-Order Method for Function-Value FreeNumerical Optimization

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

If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.

This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending

We consider black-box optimization with little assumptions on the underlying objective function. Further, we consider sampling from a distribution to obtain new candidate solutions. Under mild assumptions, solving the original black-box optimization problem coincides with optimizing a parametrized family of distributions of our choice. Choosing the family of multivariate normal distributions on the continuous search domain, a natural gradient descent on this family leads to an instantiation of the so-called CMA ES algorithm (covariance matrix adaptation evolution strategy). In this talk, the continuous black-box optimization problem will be introduced and the CMA -ES algorithm will be illustrated. The CMA -ES adapts a second-order model of the underlying objective function. On convex-quadratic functions, the resulting covariance matrix resembles the inverse Hessian matrix of the function. In contrast to Quasi-Netwon methods, this can be accomplished derivative and even function-value free. The CMA -ES reveals the same invariance properties as the famous Nelder-Mead simplex downhill method, is robust and works reliably not only in low dimensions and is surprisingly efficient on convex as well as non-convex, highly ill-conditioned problems.

This talk is part of the Microsoft Research Cambridge, public talks 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