It’s that time of year when happy friend and family gather to celebrate the entry of two singletons into forever tuple-dom. That inevitable mapping from the set of people into the set of pairs of people, with its ever changing domain…*sigh*…what a special time.

If you’re the one planning the wedding you’ll be happy to know that there are several mathematical solutions to writing the seating chart. One of them is presented on the *SAS Operations Research Blog*. You’ll also be happy to know that there’s some data been crunched on what it takes to be an upper-crust wedding.

A few years ago the techie blogger Todd Schneider wrote an N-gram analysis of the New York Times Wedding Section based on a data set he built at weddingcrunchers.com. An N-gram analysis is a tool used a lot in computational linguists and probability. It scans a text (or a corpus of texts!) for a fixed string or set of strings with length N. In this examples the strings are words that you might find in a NYT wedding announcement.

This controversial section of the paper has historically been grist for some annoyed gripes that it keeps showing the same Episcopalian Yale graduates from Greenwich, Connecticut. A while back *The Atlantic* had an article on the odds of getting into the New York Times wedding section. If you’re vying for a spot, you may want to adjust your strategy accordingly.

But back to the N-grams. Schneider uses this tool to search for trends in the NYT wedding announcements by searching common surnames, alma mater, religious affiliations, and employers. From Schneider’s analysis he determined that brides are getting older, episcopalianism is on the decline, and investment banking doesn’t have the same cache that it once did. But don’t take his word for it, you can do your own wedding N-gram searches at weddingcrunchers, for example I just found that Uber, Lyft, and Taxicab show up surprisingly frequently. How romantic.

But maybe you’re not married yet, and maybe you are a hopeless romantic hoping to meet that special someone in a taxi cab. Then what you need to consider is the stable marriage problem. This stable marriage problem asks for a way for find a stable pairing between two equally sized sets, say a set of men and a set of women (sorry this one’s a bit hetero/cis normative) given that all the people in each set have ordered preferences. There’s a solution to this problem, and that is the Gale-Shapley algorithm. That is, there is a pairing in which each person is paired with a person who they prefer and who prefers them and neither partner has the option for a “better” pairing. This is good news if you’re looking for an optimal partner (because who isn’t?) and it seems that you can run your Tinder game sort of like the Gale-Shapley algorithm.

Otherwise, *PunkRockOR* has you covered with a roundup of operations research models for finding love.

If you need me, I’ll be the one loitering around the wedding cake. Oh, and I’m for hire to write mathematically themed MOH and Best Man speeches. You can find me on Twitter @extremefriday.