I’ve got a new paper up on the arxiv. David Wilson recently posted this joint work that was one of the last things I did during my post-doc at Microsoft. It hasn’t been applied to health metrics yet, but maybe it will be. Let me tell you the story:

A spanning tree is just what it sounds like, if you know that a tree is a graph with no cycles, and make a good guess that the spanning tree is a subgraph that is a tree “spans” all the vertices in the graph. Minimum cost spanning trees come up in some very practical optimization problems, like if you want to build a electricity network without wasting wires. It was in this exact context, designing an electricity network for Moravia, that the first algorithm for finding a minimum spanning tree was developed.

The great thing about looking for a minimum spanning tree is that you don’t have to be sneaky to find it. If you repeatedly find the cheapest edge which does not create a cycle, and add that to your subgraph, then this greedy approach never goes wrong. When you can’t add any more edges, you have a minimum spanning tree. Set systems with this property have been so fun for people to study that they have an intimidating name, *matroids*. But don’t be intimidated, you can go a long way in matroids by doing a search-and-replace `s/matroid/spanning tree/`

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