In this talk we present an asynchronous multistart algorithm for identifying high-quality local minima. We highlight strong theoretical results that limit the number of local optimization runs under reasonable assumptions. Though the results are valid whether or not the derivative of the objective function exists, the method's efficient use of previously evaluated points makes it well-suited for finding minima when the objective is expensive to evaluate.
Jeffrey Larson received a Ph.D. in applied mathematics from the University of Colorado Denver. He then served as a postdoctoral researcher with the Automatic Control Laboratory in the School of Electrical Engineering at KTH Royal Institute of Technology in Stockholm, Sweden. He is currently a postdoctoral appointee with the Mathematics and Computer Science Division at Argonne National Laboratory.