With the development of big data and artificial intelligence, distributed optimization has become an indispensable tool for solving large-scale problems. Distributed optimization can be seen as a way for multi-agent systems to process distributed data through information exchange. Designing distributed optimization algorithms faces new challenges. While considering computational efficiency, distributed optimization also needs to consider communication efficiency. I will briefly introduce some methods to improve the efficiency of distributed optimization.
Ming Yan is an associate professor in the School of Data Science at The Chinese University of Hong Kong, Shenzhen. His research interests lie in computational optimization and its applications in image processing, machine learning, and other data-science problems. He received his B.S. and M.S in mathematics from University of Science and Technology of China in 2005 and 2008, respectively, and then Ph.D. in mathematics from University of California, Los Angeles in 2012. After completing his PhD, Ming Yan was a Postdoctoral Fellow at Rice University and University of California, Los Angeles until June 2015. He was a faculty member in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics at Michigan State University from 2015-2022.