This seminar will introduce 2D-MoSub, a derivative-free optimization method based on the subspace method and quadratic models, specifically tackling large-scale derivative-free problems. 2D-MoSub combines 2-dimensional quadratic interpolation models and trust-region techniques to update the points and explore the 2-dimensional subspace iteratively. Its framework includes constructing the interpolation set, building the quadratic interpolation model, performing trust-region trial steps, and updating the trust-region radius and subspace. Computation details and theoretical properties will be discussed. Numerical results demonstrate the advantage of 2D-MoSub.
Short Bio:
Pengcheng Xie, PhD (Chinese Academy of Sciences), is joining Lawrence Berkeley National Laboratory as a postdoctoral scholar specializing in mathematical optimization and numerical analysis. He has developed optimization methods, including 2D-MoSub and SUSD-TR. Pengcheng has published in major journals and presented at ISMP 2024 (upcoming), ICIAM 2023, and CSIAM 2022. He received the Hua Loo-keng scholarship in 2019 and the CAS-AMSS Presidential scholarship in 2023.