报告题目：On Wasserstein Distributionally Robust Mean Semi-Absolute Deviation Portfolio Model: Robust Selection and Efficient Computation

主 讲 人： 刘勇进 教授 (福州大学)

报告时间： 2024年9月29日 周日下午 15:30-17:30

报告地点： 数理楼221

报告摘要：

This talk focuses on the Wasserstein distributionally robust mean-lower semi-absolute deviation (DR-MLSAD) model, where the ambiguity set is a Wasserstein ball centered on the empirical distribution of the training sample. This model can be equivalently transformed into a convex problem. We develop a robust Wasserstein profile inference (RWPI) approach to determine the size of the Wasserstein radius for DR-MLSAD model. We also design an efficient proximal point dual semismooth Newton (PpdSsn) algorithm for the reformulated equivalent model. In numerical experiments, we compare the DR-MLSAD model with the radius selected by the RWPI approach to the DR-MLSAD model with the radius selected by cross-validation, the sample average approximation (SAA) of the MLSAD model, and the 1/N strategy on the real market datasets. Numerical results show that our model has better out-of-sample performance in most cases. Furthermore, we compare PpdSsn algorithm with first-order algorithms and Gurobi solver on random data. Numerical results verify the effectiveness of PpdSsn in solving large-scale DR-MLSAD problems.

报告人简介：

刘勇进，福州大学嘉锡学者特聘教授、博士生导师、福州大学数学与统计学院院长，福建省省级人才，担任福建省应用数学中心（福州大学）主任。研究兴趣主要包括：最优化理论、方法与应用，大规模数值计算，统计优化等，研究成果在包括Mathematical Programming (Series A)、SIAM Journal on Optimization、SIAM Journal on Scientific Computing等优化与计算领域国际顶级学术期刊上发表。主持国家自然科学基金4项（面上项目3项、青年基金1项），主持教育部、省重点项目等部省级纵向科研项目7项。现任中国数学会理事、中国运筹学会数学规划分会常务理事、中国运筹学会算法软件与应用分会常务理事、中国统计学会理事、中国运筹学会智能工业数据解析与优化分会理事、福建省数学学会副会长。担任国际期刊Annals of Applied Mathematics编委。