# Win \$1,000,000 by Playing Minesweeper!

Yes, you read that headline correctly: it is possible for a mathematician to win \$1,000,000 by playing Minesweeper. Here’s how:

First, the source of the \$1,000,000. In 2000, the Clay Mathematics Institute created a list of important unsolved problems in mathematics (see also Wikipedia), offering each one a prize of \$1,000,000 for a solution. Specifically, according to the rules,

[A] proposed solution must be published in a refereed mathematics publication of worldwide repute… and it must also have general acceptance in the mathematics community two years after.

Of the eight problems on the list, one (the Poincare conjecture) has been solved so far. Incredibly, the mathematician who produced the proof turned down the \$1,000,000. He was also awarded the Fields Medal, considered the Nobel Prize of mathematics, but he refused that as well.

The Millennium Problems parallel the famous 23 Hilbert problems posed by eminent mathematician David Hilbert in 1900. The Hilbert problems were unsolved at the time and were posed as a challenge to the mathematicians of the 20th century. These problems certainly generated a lot of buzz in the mathematical community, and most have been solved since 1900. (Sadly, none of the solvers won \$1,000,000 for their work!)

What does this have to do with Minesweeper?

One of the problems is the famed P vs. NP problem. From Clay Mathematics:

Suppose that you are organizing housing accommodations for a group of four hundred university students. Space is limited and only one hundred of the students will receive places in the dormitory. To complicate matters, the Dean has provided you with a list of pairs of incompatible students, and requested that no pair from this list appear in your final choice. This is an example of what computer scientists call an NP-problem, since it is easy to check if a given choice of one hundred students proposed by a coworker is satisfactory (i.e., no pair taken from your coworker’s list also appears on the list from the Dean’s office), however the task of generating such a list from scratch seems to be so hard as to be completely impractical. Indeed, the total number of ways of choosing one hundred students from the four hundred applicants is greater than the number of atoms in the known universe! Thus no future civilization could ever hope to build a supercomputer capable of solving the problem by brute force; that is, by checking every possible combination of 100 students. However, this apparent difficulty may only reflect the lack of ingenuity of your programmer. In fact, one of the outstanding problems in computer science is determining whether questions exist whose answer can be quickly checked, but which require an impossibly long time to solve by any direct procedure. Problems like the one listed above certainly seem to be of this kind, but so far no one has managed to prove that any of them really are so hard as they appear, i.e., that there really is no feasible way to generate an answer with the help of a computer. Stephen Cook and Leonid Levin formulated the P (i.e., easy to find) versus NP (i.e., easy to check) problem independently in 1971.

In 2000, Richard Kaye (University of Birmingham, UK) made connected Minesweeper to the P vs. NP problem. Famed mathematical journalist Ian Stewart did a very nice job of describing the connection between the two, so I’ll simply refer to his post for a detailed description. Dr. Kaye’s website also gives a lot of information about the connection.