Arkadi Nemirovski

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Arkadi Nemirovski
Аркадий Немировский
Born (1947-03-14) March 14, 1947 (age 79)
Moscow, Russia
Alma materMoscow State University (M.Sc 1970 & Ph.D 1973)
Kiev Institute of Cybernetics
Known forEllipsoid method
Robust optimization
Interior point method
AwardsFulkerson 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
InstitutionsGeorgia Institute of Technology
Technion – Israel Institute of Technology

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]

Biography

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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

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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

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  • co-authored with Yurii Nesterov: Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
  • co-authored with Aharon Ben-Tal: Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).[14]
  • co-authored with A. Ben-Tal and L. El Ghaoui: Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).

References

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  11. ^ Arkadi Nemirovsky and David Yudin. Problem Complexity and Method Efficiency in Optimization. John Wiley & Sons, 1983
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