Title: Semidefinite Programming and Approximation Algorithms: A Survey of Recent Results

Name: Sanjeev Arora

               Princeton University

Time:October 13th (Monday) 14:00-14:45
Location: Lecture Hall, FIT Building, Tsinghua University
Host Unit: ITCS, Tsinghua University




Abstract

Computing approximately optimal solutions is an attractive way to cope with NP-hard optimization problems. In the past decade or so, semidefinite programming or SDP (a form of convex optimization that generalizes linear programming) has emerged as a powerful tool for designing such algorithms, and the last few years have seen a profusion of results (worst-case
algorithms, average case algorithms, impossibility results, etc).

This talk will be a survey of this area and these recent results. We will see that analysing semidefinite program draws upon ideas from a variety of other areas, and has also led to new results in Mathematics. At the end we will touch upon work that greatly improves the running time of SDP-based algorithms, making them potentially quite practical.

The survey will be essentially self-contained.


Biography

Sanjeev Arora is Professor of Computer Science at Princeton University and works in computational complexity theory, approximation algorithms for NP-hard problems, geometric algorithms, and probabilistic algorithms. He has received the ACM Doctoral Dissertation Award, the SIGACT-EATCS Goedel Prize, and the Packard Fellowship.

 


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