Group for Research in Decision Analysis

Some recent work in derivative free optimization

Andrew R. Conn IBM TJ Watson Research Center, United States

Derivative free methods are amongst the most commonly used optimization techniques in practise but it is absolutely essential that they balance the geomtry of sample points with efficiency. I will present a general framework for a trust region derivative free algorithm that, for the models, assumes only that they can be made to satisfy Taylor-like error bounds. The results have broader implications (for example to, second-order trust-region methods and the connection between geometry and a (scaled) basis matrix condition number) and are of both theoretical and practical interest.

Joint work with Katya Scheinberg and Luis Vicente