Claire Mathieu

 

 


 

Claire Mathieu works on algorithms, particularly the design of approximation schemes for NP-hard problems from combinatorial optimization. She is employed by CNRS (Centre National de la Recherche Scientifique) at ENS (Ecole normale Superieure) in Paris, France. She is also a "professeur associé" at ENS. Her current interests include approximation algorithms for planar graphs and for Euclidean problems; probabilistic models for social networks; hierarchies of semi-definite programming relaxations; network tomography; scheduling to minimize energy; online algorithms; and occasional forays into algorithmic game theory.

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Dec 5th 2016

How efficiently can you pack objects into a minimum number of boxes? How well can you cluster nodes so as to cheaply separate a network into components around a few centers? These are examples of NP-hard combinatorial optimization problems. It is most likely impossible to solve such problems efficiently, so our aim is to give an approximate solution that can be computed in polynomial time and that at the same time has provable guarantees on its cost relative to the optimum.

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Dec 5th 2016

This is the continuation of Approximation algorithms, Part 1. Here you will learn linear programming duality applied to the design of some approximation algorithms, and semidefinite programming applied to Maxcut. By taking the two parts of this course, you will be exposed to a range of problems at the foundations of theoretical computer science, and to powerful design and analysis techniques.

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