| Arkadi Nemirovski | |
| Birth Date: | 14 March 1947 |
| Birth Place: | Moscow, Russia |
| Alma Mater: | Moscow State University (M.Sc 1970 & Ph.D 1973) Kiev Institute of Cybernetics |
| Known For: | Ellipsoid method Robust optimization Interior point method |
| Awards: | Fulkerson Prize (1982) Dantzig Prize (1991)[1] John von Neumann Theory Prize (2003)[2] Norbert Wiener Prize (2019)[3] The WLA Prize in Computer Science or Mathematics (2023)[4] |
| Workplaces: | Georgia Institute of Technology Technion – Israel Institute of Technology |
Arkadi Nemirovski (born March 14, 1947) is a professor at the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology.[5] He has been a leader in continuous optimization and is best known for his work on the ellipsoid method, modern interior-point methods and robust optimization.[6]
Nemirovski earned a Ph.D. in Mathematics in 1974 from Moscow State University and a Doctor of Sciences in Mathematics degree in 1990 from the Institute of Cybernetics of the Ukrainian Academy of Sciences in Kiev. He has won three prestigious prizes: the Fulkerson Prize, the George B. Dantzig Prize, and the John von Neumann Theory Prize.[7] He was elected a member of the U.S. National Academy of Engineering (NAE) in 2017 "for the development of efficient algorithms for large-scale convex optimization problems",[8] and the U.S National Academy of Sciences (NAS) in 2020.[9] In 2023, Nemirovski and Yurii Nesterov were jointly awarded the 2023 WLA Prize in Computer Science or Mathematics "for their seminal work in convex optimization theory, including the theory of self-concordant functions and interior-point methods, a complexity theory of optimization, accelerated gradient methods, and methodological advances in robust optimization."[10]
Nemirovski first proposed mirror descent along with David Yudin in 1983.[11]
His work with Yurii Nesterov in their 1994 book[12] is the first to point out that the interior point method can solve convex optimization problems, and the first to make a systematic study of semidefinite programming (SDP). Also in this book, they introduced the self-concordant functions which are useful in the analysis of Newton's method.[13]