Publications · Journal article · Oper. Res. 2022
Global convergence of stochastic gradient Hamiltonian Monte Carlo for nonconvex stochastic optimization: nonasymptotic performance bounds and momentum-based acceleration
Operations Research, 70(5), pp. 2931–2947, 2022.
In brief
Provides non-asymptotic global convergence guarantees for stochastic gradient Hamiltonian Monte Carlo on non-convex problems, quantifying when and how momentum accelerates Langevin-based optimization.
Cite
@article{gao2022sghmc,
title = {Global convergence of stochastic gradient Hamiltonian Monte Carlo for nonconvex stochastic optimization: nonasymptotic performance bounds and momentum-based acceleration},
author = {Xuefeng Gao and Mert Gürbüzbalaban and Lingjiong Zhu},
year = {2022},
journal = {Operations Research},
volume = {70(5)},
pages = {2931--2947},
doi = {10.1287/opre.2021.2162},
}Relatedsame topics