Prof Nemirovski will be hosted by TU Delft. During his visit he will give three lectures:
Abstract of these talks and information on time and the location can be found via the indicated links.
- September 17: The Matrix Cube Problem: Approximations and Applications (Stieltjes Afternoon)
- September 25: Large-scale Convex Optimization via Mirror Descent (OR seminar)
- October 2: 3D Tomography Image Reconstruction via Large-scale Convex Optimization (OR seminar)
For further information contact C. Roos.
Arkadi Nemirovski's second and third degrees (Moscow University) were in Functional Analysis; his Russian D.Sc. degree (Kiev Institute of Cybernetic) was on Complexity and efficient algorithms in Convex programming. During his Russian period, he occupied research positions in Moscow Research institutions; since his alya in 1993, he is at the Faculty of Industrial Engineering at Technion. He had held previously visiting positions at INRIA, France, and at Technion.
Prof. Nemirovski's research work is mainly in the area of Convex Programming, with emphasis on investigating complexity and on design of theoretically optimal algorithms. He was among the first to develop Information-Based Complexity Theory of Convex Optimization, with such byproducts as the Ellipsoid method (Nemirovski and Yudin, 1976) underlying the majority of modern results on efficient solvability of well-structured convex programs, e.g., on polynomial solvability of Linear Programming (Khachiyan, 1979). Prof. Nemirovski is also involved in research in Nonparametric Statistics, with emphasis on theoretically efficient robust methods for restoring noisy signals and 2D images. The focus of Prof. Nemirovski's work in recent years is in the theory and algorithmic implementation of interior-point polynomial-time methods for Convex Optimization; these methods are thought to be the most promising tool for large-scale convex programs. Jointly with Yu. Nesterov, he developed a general theory of polynomial time interior-point methods along with applications of the theory to Quadratic Quadratically Constrained, Semidefinite, Geometric Programming, etc. He participates in several projects aimed to implement the interior-point algorithms in Structural Design and Robust Control.
Prof. Nemirovski is currently Associated Editor of the journal Mathematics of Operations Research. He received the Fulkerson Prize from the Mathematical Programming Society and the American Mathematical Society for developing the Ellipsoid algorithm (1982; jointly with L.Khachiyan and D. Yudin) and the Dantzig Prize from the Mathematical Programming Society and the American Society for Industrial and Applied Mathematics for his contributions to the area of Convex Optimization (1991; jointly with M. Grotschel).
Nemirovski, A., and Yudin, D. (1983) Problem Complexity and Method Efficiency in Optimization, J.Wiley & Sons.
Nemirovskii, A. (1991), On optimality of Krylov's information when solving linear operator equations, Journal of Complexity 7, 121-130.
Nemirovskii, A. (1992), Nonparametric estimation of functions satisfying differential inequalities, in: R. Khasminskii, Ed. Advances in Soviet Mathematics, v. 12: "SelectedTopics in Nonparametric Statistics" - American Mathematical Society, Providence.
Ben-Tal, A., and Nemirovskii, A. (1994), Potential reduction polynomial time method for Truss Topology Design, SIAM J. Optimization 4, 596-612
Nesterov, Yu., and Nemirovski, A. (1994), Interior point polynomial methods in Convex Programming, SIAM Series In Applied Mathematics, Philadelphia.