报告人 (Speaker)：张进 (南方科技大学)
报告时间：2023年09月 08 日（周五） 15：30
报告摘要：Bilevel Optimization gains significant attention recently due to its various applications. Gradient-based methods guarantee theoretical convergence to stationary solutions when the lower level of the bilevel program is strongly convex (LLSC) and smooth (LLS) for fixed upper-level variable values. In this talk, we present a sequentially convergent Value Function based Difference-of-Convex Algorithm with inexactness (VF-iDCA). We show that this algorithm achieves stationary solutions without LLSC and LLS assumptions for bilevel programs from a broad class of hyperparameter tuning applications. Extensive numerical experiments justify our theoretical results and show that the proposed VF-iDCA yields superior performance.