The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group

__Soledad Villar__, *NYU*

** Date and Time: **Apr 25, 2018 (12:30 PM)

**Location: **
Orchard room (3280) at the Wisconsin Institute for Discovery Building

Quadratic assignment is a very general problem in theoretical computer science. It includes graph matching, the traveling salesman problem, and the Gromov-Hausdorff distance between finite metric spaces as particular cases. Quadratic assignment is in general NP-hard and even hard to approximate, but in fact the problem can be tractable for a large subset of instances. In this talk we present different algorithmic approaches that lead to meaningful results for different data models. A semidefinite relaxation provides a pseudometric that can be computed in polynomial-time and has similar topological properties to the GH distance. A projected power iteration algorithm succeeds at aligning noisy networks. And a graph neural network can actually learn an algorithm to solve network alignment and the traveling salesman problem from solved problem instances.