Handling Noise in Model-Based Derivative-Free Optimization

Derivative-Free Optimization (DFO) refers to optimization algorithms that do not require any derivative information, typically from the objective. Among the most practically successful types of DFO are model-based methods, which attempt to construct local interpolation models for use in algorithms that mimic successful derivative-based optimization algorithms. In this talk, I will outline some recent theoretical results for handling of noisy objectives in model-based DFO, considering both generic, bounded noise, and stochastic noise with constraints. This is joint work with Nicole Felice and Sara Shashaani (North Carolina State University).

Link to workshop website