Arkadi Nemirovski

Arkadi Nemirovski (Russian: Аркадий Немировский; 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]

Arkadi Nemirovski
Аркадий Немировский
Born	(1947-03-14) March 14, 1947 (age 78) 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]
Scientific career
Institutions	Georgia Institute of Technology Technion – Israel Institute of Technology

Biography

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]

Academic work

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]

Books

• co-authored with Yurii Nesterov: Interior-Point Polynomial Algorithms in Convex Programming. Society for Industrial and Applied Mathematics. 1994. ISBN 978-0898715156.
• co-authored with Aharon Ben-Tal: Lectures on Modern Convex Optimization. Society for Industrial and Applied Mathematics. 2001. ISBN 978-0-89871-491-3.^[14]
• co-authored with A. Ben-Tal and L. El Ghaoui: Robust Optimization. Princeton University Press. 2009. ISBN 978-0-691-14368-2.