Distinguished Visiting Professor: Yurii Nesterov


Ph.D. Applied Mathematics, Institute of Control Sciences, Moscow, USSR

MS Applied Mathematics, Moscow State University, USSR

Undergraduate, Chair of Operations Research, Moscow State University, USSR

Fields of Interest

Optimization, Numerical Analysis, Control Theory

Significant Recognition

Lanchester Prize, INFORMS, 2022

Elected to the US National Academy of Sciences, 2022

Elected to European Academy of Sciences, 2021

Euro Gold Medal, The Association of European Research Societies, 2016

SIAM Outstanding Paper Award, SIAM, 2014

John von Neumann Theory Prize, INFORMS, 2009

Dantzig Prize, Mathematical Programming Society and SIAM, August 2000

Recent Publications

N. Doikov, Yu. Nesterov. Affine-invariant contracting-point methods for convex optimization. CORE Discussion Papers 2020/29 (2020). Published in Mathematical Programming, DOI 10.1007/s10107-
021-01761-9 (January 2022).

Yu. Nesterov. Inexact high-order proximal-point methods with auxiliary search procedure. SIOPT. Accepted June 2021.

Yu. Nesterov. Inexact Accelerated High-Order Proximal-Point Methods. Mathematical Programming. DOI 10.1007/s10107-021-01727-x (2021).

A. Rodomanov, and Yu. Nesterov. Greedy Quasi-Newton Methods with Explicit Superlinear Convergence. SIOPT, 31(1), 785-811 (2021).

Yu. Nesterov. Superfast second-order methods for Unconstrained Convex Optimization. JOTA, 191, 1-30 (2021)

N. Doikov, Yu. Nesterov. Optimization methods for fully composite problems. CORE Discussion Papers 2021/01 (2021).

N. Doikov, and Yu. Nesterov. Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method Journal of Optimization Theory and Applications, 189, 317{339 (2021), DOI 10.1007/s10957-021-01838-7.

Yu. Nesterov. Implementable tensor methods in unconstrained convex optimization. Mathematical Programming, DOI 10.1007/s10107-019-01449-1, 186: 157-183 (2021)