Trust Region Methods for Nonconvex Stochastic Optimization beyond Lipschitz Smoothness

This AAAI 2024 paper studies stochastic nonconvex optimization beyond the standard globally Lipschitz-gradient assumption. It analyzes trust-region methods under a generalized smoothness setting motivated by deep learning and distributionally robust optimization, showing how first-order trust-region methods unify normalized and clipped stochastic-gradient updates and how second-order variants can obtain stronger stationarity guarantees with variance reduction.