Zaiwen Wen

Wen Zaiwen, Professor at Peking University. He mainly studies optimization algorithms and theory and their applications in machine learning. Recently, he has contributed on algorithms for nonsmooth and nonconvex optimization. Together with his coauthors, a class of semi-smooth Newton methods is developed, and a framework on proving global convergence and R-superlinear local convergence with high probability is established. Two textbooks including "Optimization: Modeling, Algorithms and Theory" were published in 2021. They have been widely used by majors related to mathematics, statistics, data science, artificial intelligence in more than 100 universities. He was awarded the China Youth Science and Technology Award in 2016 and Beijing Outstanding Youth Zhongguancun Award in 2020. He was funded by the National Ten Thousand Talents Program for Science and Technology Innovation. He is an associate editor of "Communications in Mathematics and Statistics", "Journal of the Operations Research Society of China", "Journal of Computational Mathematics" and a technical editor of "Mathematical Programming Computation".

Homepage: https://bicmr.pku.edu.cn/~wenzw/

文再文，北京大学教授。主要研究最优化算法与理论及其在机器学习中的应用。近年来在非光滑非凸优化算法方面取得了一些成果，与合作者发展了半光滑牛顿类算法，建立了全局收敛和以很高概率R-超线性收敛的分析框架。2021年出版《最优化：建模、算法与理论》等两本教材，得到了百余所高校采用，涉及数学、统计、数据科学、人工智能等专业。2016年获中国青年科技奖，2020年获国家万人计划科技创新领军人才和北京市杰出青年中关村奖。现为“Communications in Mathematics and Statistics ”，“Journal of the Operations Research Society of China”, “Journal of Computational Mathematics”的编委和“Mathematical Programming Computation”的技术编委。