Derivative-free optimisation for least-squares problems [slides available]


Least-squares problems (such as parameter estimation) are ubiquitous across quantitative disciplines. Optimisation algorithms for solving such problems are numerous and well-established. However, in cases where models are computationally expensive, black box, or noisy, classical algorithms can be impractical or even fail. Derivative-free optimisation (DFO) methods provide an alternative approach which can handle these settings. In this talk, Lindon will introduce a derivative-free version of the classical Gauss-Newton method, discuss its theoretical guarantees and software implementation, and describe applications of this technique to parameter estimation of global climate models and image reconstruction.

16 Apr 2020
University of New South Wales
Lindon Roberts

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