The advent of computational science has unveiled large classes of nonlinear optimization problems that present opportunities for both application advancement and fundamental mathematical and computational research. For many of these problems, derivative-free optimization is a natural gateway to increasingly complex and increasingly efficient mathematical optimization algorithms. In this talk, we will review the mathematical background underpinning derivative-free trust-region algorithms, with a particular emphasis on achieving practical performance without sacrificing asymptotic guarantees. We will summarize our work in addressing diverse applications that highlight the breadth of opportunities for nonlinear optimization. I will conclude with some thoughts on future research directions in applied mathematics and statistics in the context of the US Department of Energy.
 
Speaker Biography:
Stefan Wild is a Computational Mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. His primary research focus is on algorithms and software for challenging numerical optimization problems. Prior to his staff appointment, he was an Argonne Director's Postdoctoral Fellow and a DOE Computational Science Graduate Fellow. He obtained his Ph.D. in operations research from Cornell University and B.S. and M.S. degrees in applied mathematics from the University of Colorado-Boulder.