Exploiting Problem-Specific Knowledge and Computational Resources in Derivative-Free Optimization

This talk begins with a comparison of methods for optimizing computationally expensive functions which lack reliable gradient information. We highlight recently developed algorithms that utilize the structure of common problems, and demonstrate their efficacy on relevant applications. We then show how such algorithms can be incorporated into an asynchronous, multi-start framework. Theoretical results and practical performance of such a framework concludes the talk.

Event Topic

Data Science