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An Accelerated Proximal Alternating Direction Method
of Multipliers for Robust Fused Lasso
靳正芬
（河南科技大学 副教授）
报告时间：2023年6月30日 星期五 上午10:00-11:00
报告地点：沙河校区E404
报告摘要：In the era of big data, much of the data is susceptible to noise with heavy-tailed distribution. Fused Lasso can effectively handle high dimensional sparse data with strong correlation between two adjacent variables under known Gaussian noise. However, it has poor robustness to non-Gaussian noise with heavy-tailed distribution. Robust fused Lasso with 𝑙1 norm loss function can overcome the drawback of fused Lasso when noise is heavy-tailed distribution. But the key challenge for solving this model is non-smoothness and its non-separability. Therefore, in this paper, we first deform the robust fused Lasso into an easily solvable form, which changes the three-block objective function to a two-block form. Then, we propose an accelerated proximal alternating direction method of multipliers (APADMM) with an additional update step, which is based on a new PADMM that changes the Lagrangian multiplier term update. Furthermore, we give the 𝑂(1/𝐾) nonergodic convergence rate analysis of the proposed APADMM. Finally, numerical results show that the proposed new PADMM and APADMM have better performance than other existing ADMM solvers.
报告人简介：靳正芬，河南科技大学，副教授。2021年5月至今，在best365网页版登录从事博士后研究工作，合作导师韩德仁教授。研究方向是最优化理论、方法及其应用、大规模稀疏与低秩优化、统计优化。主持1项国家自然科学基金青年项目和1项河南省自然科学基金青年项目，参与5项国家自然科学基金项目。在Numerical Linear Algebra with Applications, Journal of Scientific Computing, Journal of Computational and Applied Mathematics和Applied Mathematical Modelling等国外著名期刊上发表论文10余篇。现为中国运筹学会数学规划分会青年理事，中国运筹学会宣传工作委员会委员，河南省运筹学会组织工作委员会委员。
邀请人：谢家新
