Selected Applications of Convex Optimization - Li Li

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Résumé :
This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.

Biographie:
Li Li received the B.S. and M.S. degrees from Shenyang Agriculture University, China, in 1996 and 1999, respectively, and the Ph.D. degree from Shenyang Institute of Automation, Chinese Academy of Sciences, in 2003. She joined Tongji University, Shanghai, China, in 2003, and is presently Professor of Control Science and Engineering. Her research interests are in data-driven modeling and optimization, intelligent manufacturing, artificial intelligence, and computational intelligence. Li Li has received numerous awards and honors, including but not limited to the innovation team award, first prize of technical invention, best application paper award, best paper nomination award, etc. In addition, she has published four monographs and more than 70 academic papers and has presided over or participated in more than ten national scientific research projects. Kuo-Yi Lin received the B.S. degree in Statistics from Cheng Kung University, Taiwan, China, in 2007 and an M.S. degree andPh.D. degree in Industrial Engineering and Engineering Management from Tsing Hua University, Taiwan, China, in 2009 and 2014. He is a director of China Excellent Business Decision Making Society, member of Intelligent Simulation Optimization and Scheduling Committee of China Simulation Society, member of Natural Computing and Digital Intelligent City Committee of China Artificial Intelligence Society, member of Industrial Big Data and Intelligent System Branch of China Mechanical Engineering Society. He is mainly engaged in intelligent manufacturing, federated learning, quantum algorithm and transfer learning. Qingyun Yu received the B.S. degree from Jiangnan University, China, in 2013 and Ph.D. degree in College of Electronic and Information Engineering from Tongji University, China, in 2021. She is now a postdoctoral fellow at the College of Electronic and Information Engineering, Tongji University. Her research interests are in data-driven modeling and optimization, intelligent manufacturing, and computational intelligence. She has published two monographs, more than ten academic papers, one patent, and one software copyright and has participated in two national scientific research projects. Prof. Fei QIAO received her MS in Control Engineering (1993) and PhD in Management Engineering (1997) from Tongji University, Shanghai, China. Since 1993 she joined Tongji University, where she is currently a full professor with the School of Electronics and Information Engineering. In the past 30 years, she researched extensively in areas of intelligent manufacturing operation optimization, production planning and scheduling, sustainable manufacturing scheduling, etc. She has published five books and over 100 research papers in academic journals and conferences. Prof. Qiao was a recipient of the Humboldt Scholarship from the Alexander von Humboldt Foundation (Germany) and the Excellent Talent of New Century from Ministry of Education (China). She is an Executive Director and the Deputy Secretary of Chinese Automation Association, and the Deputy Director of Shanghai Society of System Engineering. Yumin Ma received the B.S, M.S and Ph.D degrees from Tongji University, Shanghai, China, in 1994, 1999 and 2002, respectively. She joined Tongji University in 2002, and is presently Associate Professor of System Engineering. Her research interests are in production planning & scheduling in manufacturing system and intelligent manufacturing. She has published three monographs and more than 50 academic papers....

Sommaire:
Preliminary Knowledge.- Support Vector Machines.- Parameter Estimations.- Norm Approximation and Regulariztion.- Semi-Definite Programing and Linear Matrix Inequalities.- Convex Relaxation.- Geometric Problems.