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Home > Laureates > Yurii NESTEROV
2023 WLA Prize Laureates

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"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."
The 2023 WLA Prize Laureate in Computer Science or Mathematics
Professor Emeritus and Senior Scientific Researcher, Center for Operations Research & Econometrics and Mathematical Engineering Department, Université Catholique de Louvain, Belgium
About the Laureate

Yurii Nesterov shares the 2023 WLA Prize in Computer Science or Mathematics with Arkadi Nemirovski.

Prof. Yurii Nesterov has been a world-leading researcher in Convex Optimization for four decades. His first important results were related to Fast Gradient Methods (FGM). This contribution is increasingly important up to now, finding more and more applications in Machine Learning and Artificial Intelligence.
The next fundamental development (jointly with Prof. Arkadi Nemirovski) was the theory of polynomial-time interior-point methods for Convex Optimization. By this theory, any convex optimization problem can be solved in polynomial time using a second-order method, endowed with a self-concordant barrier for its feasible set. A good barrier can be found by reformulating the initial problem. This was the first example of Structural Optimization, successfully competing with the standard Black-Box settings. It was extended onto the conic primal-dual formulations, which support the most efficient methods for solving Linear Matrix Inequalities, the main tool of modern Control Theory.
His further breakthrough was related to Smoothing Technique. It was shown that any piece-wise linear function with an explicit max-representation can be efficiently approximated by a differentiable convex function. By minimizing the latter one by FGM, it is possible to obtain an algorithm, which overpasses the Black-Box lower complexity bounds by the orders of magnitude.
In the recent years, he is working on efficient versions of the higher-order methods. New Cubic Regularization of Newton Method became the first second-order scheme with provable global complexity bounds. An important result on convexity of augmented Taylor polynomials paved the way for development of higher-order tensor methods with very high rates of convergence. Some implementations of the third order methods are now the most efficient in Optimization.


1977, Master's Degree in Applied Mathematics, Moscow State University, USSR
1984, Ph. D. in Applied Mathematics, Institute of Control Sciences, Moscow, USSR
2014, 2nd doctoral degree in Applied Mathematics, Moscow Institute of Physics and Technology, Russia

Professional Experience

1977-2000, Research Associate of different levels at Central Economics and Mathematics Institute of the Russian Academy of Sciences
1992-1993, Visiting Professor, University of Geneva, Switzerland
1993-2000, Visiting Professor, Center for Operations Research and Econometrics (CORE), Université Catholique de Louvain, Belgium  
2000-present, Professor, Full Professor, Professor Emeritus and Senior Scientific Researcher, Mathematical Engineering (INMA) / Center for Operations Research and Econometrics (CORE) , Université Catholique de Louvain, Belgium

Major Awards and Honors

2000, George B. Dantzig Prize (Mathematical Optimization Society, MOS; Society for Industrial and Applied Mathematics, SIAM)
2009, John von Neumann Theory Prize (The Institute for Operations Research and the Management Sciences, INFORMS)
2009, Charles Broyden Prize for best paper in Optimization Methods and Software
2014, SIAM Outstanding Paper Award
2016, EURO Gold Medal (The Association of European Operational Research Societies, EURO)
2018-2023, ERC Advanced Grant (European Research Council)
2021, Member, Academia Europaea
2022, Member, National Academy of Sciences, U.S.A.
2022, Frederick W. Lanchester Prize (The Institute for Operations Research and the Management Sciences, INFORMS)

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