4 papers
Uniform-in-time propagation of chaos for Consensus-Based Optimization
Nicolai Gerber, Franca Hoffmann, Dohyeon Kim +1
We study the derivative-free global optimization algorithm Consensus-Based Optimization (CBO), establishing uniform-in-time propagation of chaos as well as an almost uniform-in-tim…
Graph Laplacian-based Bayesian Multi-fidelity Modeling
Orazio Pinti, Jeremy M. Budd, Franca Hoffmann +1
We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Lapl…
MirrorCBO: A consensus-based optimization method in the spirit of mirror descent
Leon Bungert, Franca Hoffmann, Dohyeon Kim +1
In this work we propose MirrorCBO, a consensus-based optimization (CBO) method which generalizes standard CBO in the same way that mirror descent generalizes gradient descent. For…
Mean-field limits for Consensus-Based Optimization and Sampling
Nicolai Jurek Gerber, Franca Hoffmann, Urbain Vaes
For algorithms based on interacting particle systems that admit a mean-field description, convergence analysis is often more accessible at the mean-field level. In order to transfe…