Search Results for author:
Found 3 papers, 2 papers with code
Ensemble-based gradient inference for particle methods in optimization and sampling
We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions {from a given ensemble of particles}.
Optimal control for interacting particle systems driven by neural networks
We propose a neural network approach to model general interaction dynamics and an adjoint based stochastic gradient descent algorithm to calibrate its parameters.
Optimization and Control
An analytical framework for a consensus-based global optimization method
This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions.
Analysis of PDEs
