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WLA Prize Laureates Lectures by 2023 Winners in Computer Science or Mathematics

Date Nov. 23, 2023

Click here to watch the video of the lectures on the official website of the WLA Forum.

The 2023 WLA Prize Laureates in Computer Science or Mathematics delivered academic lectures at the recently concluded 6th WLA Forum in Shanghai. The lectures are part of the WLA Prize feature events.

Date & Time: 09:00-10:35, Nov. 5, 2023

Venue: Permanent Site of the WLA Forum(Lingang Center), Shanghai

Host: Michael I. Jordan, Chair of 2023 WLA Prize Selection Committee (Computer Science or Mathematics); Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Sciences & Department of Statistics, UC Berkeley

Speakers: 2023 WLA Prize laureates in Computer Science or Mathematics
Yurii Nesterov
Professor Emeritus and Senior Scientific Researcher, Center for Operations Research & Econometrics and Mathematical Engineering Department, Université Catholique de Louvain, Belgium
Optimization, the Philosophical Background of Artificial Intelligence 
Abstract: We discuss new challenges in the modern Science, created by Artificial Intelligence (AI). Indeed, AI requires a system of new sciences, mainly based on computational models. Their development has already started by the progress in Computational Mathematics. In this new reality, Optimization plays an important role, helping the other fields with finding tractable models and efficient methods, and significantly increasing their predictive power. We support our conclusions by several examples of efficient optimization schemes related to human activity.

Arkadi Nemivorski
John P. Hunter, Jr. Chair Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, U.S.A.
Topics in Convex Optimization
We present an informal "executive summary" of "well-structured" convex optimization problems and their "structure-revealing" conic representations, primarily polyhedral, conic quadratic, and semidefinite ones. As a matter of fact, basically all convex problems arising in applications admit representations of this type, making the problems amenable to theoretically (and to some extent also practically) efficient solution algorithms capable of approximating to whatever high accuracy globally optimal solutions in a reasonable time. Another advantage of conic representations is the existence of a fully algorithmic duality theory, entirely similar to Linear Programming duality. Conic duality is indispensable in algorithmic design and analysis, on the one hand, and offers powerful tools for instructive processing convex optimization models "on paper," on the other hand. In the lecture, we entirely omit algorithmic issues (too technical for informal presentation) and focus on Conic Programming duality, revealing its surprisingly simply-looking geometry and briefly outlining several applications, including those in Truss Topology Design, Robust Optimization, synthesis of robust linear controllers, and Signal Estimation.

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