### Guillaume Sagnol (ZIB - Konrad Zuse Zentrum für Informationstechnik Berlin)

#### Algorithmic aspects of large scale optimal experimental design

*Wednesday 8 February 2012 at 15.30, JCMB 6206*

##### Abstract

The theory of optimal experimental design plays a central role in statistics.
It studies how to best select experiments in order to estimate a set of unknown
parameters. A common approach is to distribute the experimental effort so as to
minimize a scalar function measuring the size of the confidence ellipsoids of
an estimator for the unknown parameter. This leads to a convex optimization
problem involving a spectral function which is applied to the information
matrix of the experiments.

While new applications of experimental design arise, in particular for the
monitoring of large communication networks, there is a need for algorithms
that can solve very large instances of this kind of optimization problems. In
this talk we shall review the classical algorithms, present recent developments
and explore new directions to compute optimal designs.

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