Embedding of metric spaces in R^n

Assaf Naor
Microsoft Research

In this talk we will survey some of the main results in the theory of bi-Lipschitz embeddings of metric spaces into certain ā€œsimpleā€ normed spaces, such as Euclidean space. We will also sketch some of the techniques used in this field, and some of the many applications of low-distortion embeddings to geometric analysis and the design of approximation algorithms for NP-hard problems. The techniques used in this field draw on analysis, geometry, combinatorics and probability. No prerequisites beyond first year graduate mathematics will be assumed.


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