A PDE Framework of Consensus-Based Optimization for Objectives with Multiple Global Minimizers

Abstract

Consensus-based optimization (CBO) is an agent-based derivative-free method for non-smooth global optimization that has been introduced in 2017, leveraging a surprising interplay between stochastic exploration and Laplace principle. In addition to its versatility and effectiveness in handling high-dimensional, non-convex, and non-smooth optimization problems, this approach lends itself well to theoretical analysis. Indeed, its dynamics is governed by a degenerate nonlinear Fokker--Planck equation, whose large time behavior explains the convergence of the method. Recent results provide guarantees of convergence under the restrictive assumption of a unique global minimizer for the objective function. In this work, we propose a novel and simple variation of CBO to tackle non-convex optimization problems with multiple global minimizers. Despite the simplicity of this new model, its analysis is particularly challenging because of its nonlinearity and nonlocal nature. We prove the existence of solutions of the corresponding nonlinear Fokker--Planck equation and we show exponential concentration in time to the set of minimizers made of multiple smooth, convex, and compact components. Our proofs require combining several ingredients, such as delicate geometrical arguments, new variants of a quantitative Laplace principle, ad hoc regularizations and approximations, and regularity theory for parabolic equations. Ultimately, this result suggests that the corresponding CBO algorithm, formulated as an Euler-Maruyama discretization of the underlying empirical stochastic process, tends to converge to multiple global minimizers.