Improving the scalability of derivative-free optimization for nonlinear least-squares problems

Abstract

In existing techniques for model-based derivative-free optimization, the computational cost of constructing local models and Lagrange polynomials can be high. As a result, these algorithms are not as suitable for large-scale problems as derivative-based methods. In this talk, I will introduce a derivative-free method based on exploration of random subspaces, suitable for nonlinear least-squares problems. This method has a substantially reduced computational cost (in terms of linear algebra), while still making progress using few objective evaluations.

Lindon Roberts

Lecturer

My research is in numerical analysis, particularly nonconvex and derivative-free optimization.