报告题目：Riemannian Optimization: A Proximal Newton Method

报告时间：2024-01-12   10:00-11:00

报 告 人 ：黄文教授   厦门大学

报告地点：理学院东北楼三楼报告厅（302）

Abstract：Optimization on Riemannian manifolds, also called Riemannian optimization, considers finding an optimum of a real-valued function defined on a Riemannian manifold. Riemannian optimization has been a topic of much interest over the past few years due to many important applications. In this presentation, the framework of Riemannian optimization is introduced, and the current state of Riemannian optimization algorithms are briefly reviewed. To show a research focus and difficulties of Riemannian optimization, we generalize the proximal Newton method to the Riemannian setting and the difficulties therein are highlighted. The convergence results are given. Numerical experiments verify that the Riemannian proximal Newton method converges superlinearly locally.