Model Construction for Convex-Constrained Derivative-Free Optimization [slides available]

Abstract

In model-based derivative-free optimisation algorithms, black-box functions are typically approximated using polynomial interpolation models. Most existing model-based DFO methods for constrained optimisation assume the ability to construct sufficiently accurate interpolation models, but this is not always achievable when sampling only feasible points. In this talk, I will outline a new approximation theory for linear and quadratic interpolation models in the presence of convex constraints.

Lindon Roberts

Lecturer

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