Yurii Nesterov

Yurii Nesterov is a Russian mathematician, an internationally recognized expert in convex optimization, especially in the development of efficient algorithms and numerical optimization analysis. He is currently a professor at the University of Louvain (UCLouvain).

Yurii Nesterov
2005 in Oberwolfach
Born (1956-01-25) January 25, 1956
Moscow, USSR
CitizenshipBelgium
Alma materMoscow State University (1977)
Awards
Scientific career
Fields
Institutions
Doctoral advisorBoris Polyak

Biography

In 1977, Yurii Nesterov graduated in applied mathematics at Moscow State University. From 1977 to 1992 he was a researcher at the Central Economic Mathematical Institute of the Russian Academy of Sciences. Since 1993, he has been working at UCLouvain, specifically in the Department of Mathematical Engineering from the Louvain School of Engineering, Center for Operations Research and Econometrics.

In 2000, Nesterov received the Dantzig Prize.[1]

In 2009, Nesterov won the John von Neumann Theory Prize.[2]

In 2016, Nesterov received the EURO Gold Medal.[3]

Academic work

Nesterov is most famous for his work in convex optimization, including his 2004 book, considered a canonical reference on the subject.[4] His main novel contribution is an accelerated version of gradient descent that converges considerably faster than ordinary gradient descent (commonly referred as Nesterov momentum or Nesterov accelerated gradient, in short — NAG).[5][6][7][8]

His work with Arkadi Nemirovski in the 1994 book[9] 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.[10]

gollark: * tweaking
gollark: You would just run the same algorithms but on the surface and surface coordinates instead. Maybe with minor tweaing.
gollark: Latitude and longitude.
gollark: Easy enough, split it into nonrectangular pixels.
gollark: You mean you want a format to store images in the shape of a sphere?

References
  1. "The George B. Dantzig Prize". 2000. Retrieved December 12, 2014.
  2. "John Von Neumann Theorey Prize". 2009. Retrieved June 4, 2014.
  3. "EURO Gold Medal". 2016. Retrieved August 20, 2016.
  4. Nesterov, Yurii (2004). Introductory lectures on convex optimization : A basic course. Kluwer Academic Publishers. CiteSeerX 10.1.1.693.855. ISBN 978-1402075537.
  5. Nesterov, Y (1983). "A method for unconstrained convex minimization problem with the rate of convergence ". Doklady AN USSR. 269: 543–547.
  6. Bubeck, Sebastien (April 1, 2013). "ORF523: Nesterov's Accelerated Gradient Descent". Retrieved June 4, 2014.
  7. Bubeck, Sebastien (March 6, 2014). "Nesterov's Accelerated Gradient Descent for Smooth and Strongly Convex Optimization". Retrieved June 4, 2014.
  8. "The Zen of Gradient Descent".
  9. Nesterov, Yurii; Arkadii, Nemirovskii (1995). Interior-Point Polynomial Algorithms in Convex Programming. Society for Industrial and Applied Mathematics. ISBN 978-0898715156.
  10. Boyd, Stephen P.; Vandenberghe, Lieven (2004). Convex Optimization (PDF). Cambridge University Press. ISBN 978-0-521-83378-3. Retrieved October 15, 2011.
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