报告人：湘潭大学 彭拯教授
报告时间：2025年11月15日10:00
报告地点：科技园阳光楼南815
报告题目：Faster stochastic cubic regularized Newton methods with momentum
报告摘要：
We propose faster SCRN methods that incorporate gradient estimation with small, controlled errors and Hessian estimation with momentum-based variance reduction. These methods are particularly effective for problems where the gradient can be estimated accurately and at low cost, whereas accurate estimation of the Hessian is expensive. Under mild assumptions, we establish the iteration complexity of our SCRN methods by analyzing the descent of a novel potential sequence. Finally, numerical experiments show that our SCRN methods can achieve comparable performance to deterministic CRN methods and vastly outperform first-order methods in terms of both iteration counts and solution quality.
报告人简介：
彭拯，湘潭大学教授，博士生导师。主要从事数学优化理论、算法及其应用研究，当前研究兴趣在于流形优化与流形学习、超大规模集成电路EDA、下一代通信网络、新能源电力系统等理论与实际应用中的大规模非凸非光滑优化问题求解算法，尤其关注随机优化算法与非单调优化算法相关研究。主持国家重要科研项目6项，当前兼任中国运筹学会常务理事、湖南省运筹学会副理事长，中国运筹学会算法软件及其应用分会常务理事和数学规划分会理事。
