615	RWTH Publication No: 821206        2021
TITLE	Binary interaction methods for high dimensional global optimization and machine learning
AUTHORS	Alessandro Benfenati, Giacomo Borghi, Lorenzo Pareschi
ABSTRACT	In this work we introduce a new class of gradient-free global optimization methods based on a binary interaction dynamics governed by a Boltzmann type equation. In each interaction the particles act taking into account both the best microscopic binary position and the best macroscopic collective position. For the resulting kinetic optimization methods, convergence to the global minimizer is guaranteed for a large class of functions under appropriate parameter constraints that do not depend on the dimension of the problem. In the mean-field limit we show that the resulting Fokker-Planck partial differential equations generalize the current class of consensus based optimization (CBO) methods. Algorithmic implementations inspired by the well-known direct simulation Monte Carlo methods in kinetic theory are derived and discussed. Several examples on prototype test functions for global optimization are reported including an application to machine learning.
KEYWORDS	gradient-free methods, global optimization, Boltzmann equation, mean-field limit, consensus-based optimization, machine learning
DOI	10.1007/s00245-022-09836-5
PUBLICATION	Appl. Math. Optim. 86 2022, no. 1, Paper No. 9