Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models.

摘要

This project focused on development of novel derivative free optimization methods that rely on recent techniques and models from statistical learning. The main idea of these methods is to build local models of the objective function from randomly sampled data points. This approach has many benefits, in that it allows us to construct fairly accurate models with relatively small number of samples. The key difference with the deterministic sampling approaches is that these accurate models are constructed with some high probability, but not always. Moreover, it is not known, when these models are accurate. Only the probability of an accurate model occurring is known. Under these conditions, novel convergence theory needed to be developed, which has been the focus of our research.