We improve recently introduced consensus-based optimization method, proposed in [R.
Pinnau, C. Totzeck, O. Tse, S. Martin, Math. Models Methods Appl. Sci. 27 (2017) 183–204] …

We present the asymptotic transitions from microscopic to macroscopic physics, their
computational challenges and the asymptotic-preserving (AP) strategies to compute …

In this paper we provide a rigorous convergence analysis for the renowned particle swarm
optimization method by using tools from stochastic calculus and the analysis of partial …

We investigate the implementation of a new stochastic Kuramoto-Vicsek-type model for
global optimization of nonconvex functions on the sphere. This model belongs to the class of …

The recent random batch Ewald algorithm, originating from a stochastic approximation,
performs 1 order of magnitude faster than the mainstream algorithms such as the particle …

In this chapter we give an overview of the consensus-based global optimization algorithm
and its recent variants. We recall the formulation and analytical results of the original model …

In this paper, we consider a continuous description based on stochastic differential
equations of the popular particle swarm optimization (PSO) process for solving global …

In this work we are interested in the construction of numerical methods for high-dimensional
constrained nonlinear optimization problems by particle-based gradient-free techniques. A …

Abstract Stein Variational Gradient Descent (SVGD) is a popular particle-based variational
inference algorithm with impressive empirical performance across various domains …

We develop a random batch Ewald (RBE) method for molecular dynamics simulations of
particle systems with long-range Coulomb interactions, which achieves an O(N) complexity …