  In this paper we provide a novel analytical perspective on the theoretical
understanding of gradient-based learning algorithms by interpreting
consensus-based optimization (CBO), a recently proposed multi-particle
derivative-free optimization method, as a stochastic relaxation of gradient
descent. Remarkably, we observe that through communication of the particles,
CBO exhibits a stochastic gradient descent (SGD)-like behavior despite solely
relying on evaluations of the objective function. The fundamental value of such
link between CBO and SGD lies in the fact that CBO is provably globally
convergent to global minimizers for ample classes of nonsmooth and nonconvex
objective functions, hence, on the one side, offering a novel explanation for
the success of stochastic relaxations of gradient descent. On the other side,
contrary to the conventional wisdom for which zero-order methods ought to be
inefficient or not to possess generalization abilities, our results unveil an
intrinsic gradient descent nature of such heuristics. This viewpoint
furthermore complements previous insights into the working principles of CBO,
which describe the dynamics in the mean-field limit through a nonlinear
nonlocal partial differential equation that allows to alleviate complexities of
the nonconvex function landscape. Our proofs leverage a completely nonsmooth
analysis, which combines a novel quantitative version of the Laplace principle
(log-sum-exp trick) and the minimizing movement scheme (proximal iteration). In
doing so, we furnish useful and precise insights that explain how stochastic
perturbations of gradient descent overcome energy barriers and reach deep
levels of nonconvex functions. Instructive numerical illustrations support the
provided theoretical insights.
