Arkadi Nemirovski has been awarded the 2019 Norbert Wiener Prize in Applied Mathematics. The prize is awarded once every 3 years by AMS and SIAM for an outstanding contribution to applied mathematics in the highest and broadest sense. The citation reads:
The 2019 Norbert Wiener Prize in Applied Mathematics will be awarded to Professor Arkadi Nemirovski for his fundamental contributions to high-dimensional optimization and for his discovery of key phenomena in the theory of signal estimation and recovery.
A powerful and original developer of the mathematics of high-dimensional optimization, Nemirovski, with D. Yudin, invented the ellipsoid method used by Leonid Khachiyan to show for the first time that linear programs can be solved in polynomial time. With Yurii Nesterov, he extended interior‐point methods in the style of Narendra Karmarkar to general nonlinear convex optimization. This foundational work established that a rich class of convex problems called semidefinite programs are solvable in polynomial time. Semidefinite programs are now routinely used to model concrete applied problems and to study deep problems in theoretical computational complexity. A third breakthrough, with Aharon Ben-Tal, was the invention of methods of robust optimization to address problems in which the solution may be very sensitive to problem data. Nemirovski also and rather amazingly made seminal contributions in mathematical statistics, establishing the optimal rates at which certain
classes of nonparametric signals can be recovered from noisy data and investigating limits of performance for estimation of nonlinear functionals from noisy measurement. His contributions have become bedrock standards with tremendous theoretical and practical impact on the field of continuous optimization and beyond.
Find out more about the Wiener Prize and see previous recipients here.
This story originally appeared on the ACO gatech webpage