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Abstract:

This presentation addresses an optimization problem where the cost function is the expectation of a random mapping.  Two approaches are developed to tackle the problem.  They are based on the approximation of the objective function by consensus-based particle optimization methods on the search space.  The resulting methods are mathematically analyzed using a mean-field approximation, therefore establishing a connection.  Several numerical experiments show the validity of the proposed algorithms and investigate their rates of convergence.  This talk is based on a joint work with Professor Michael Herty.

Speaker’s Bio:

Sabrina Bonandin is a Ph.D. student under the supervision of Professor Michael Herty at Rheinisch-Westfälische Technische Hochschule Aachen University, Germany.  She completed her bachelor’s degree and master’s degree in mathematics at the University of Pavia, Italy, where she developed a strong interest in multiscale modeling and related numerical methods.  Her current research aims to develop innovative algorithms inspired by collective behaviors in nature to address complex optimization challenges such as swarm-based optimization methods.  Specifically, her research is focused on the resolution of optimization problems that encompass uncertainties in their formulation.