Introduction to Model-Based Derivative-Free Optimization

The field of derivative-free optimization (DFO) studies algorithms for nonlinear optimization that do not rely on the availability of gradient or Hessian information.

It is primarily designed for settings when functions are black-box, expensive to evaluate and/or noisy.

A widely used and studied class of DFO methods for local optimization is model-based DFO, where the general principles from derivative-based nonlinear optimization algorithms are followed, but local Taylor-type approximations are replaced with alternative local models constructed by interpolation.

This document provides an overview of the basic algorithms and analysis for model-based DFO, covering worst-case complexity, approximation theory for polynomial interpolation models, and extensions to constrained and noisy problems.