Constraint Programming is a style of problem solving where the properties of a solution are first identified, and a large space of solutions is searched through to find the best. Good constraint programming depends on modeling the problem well, and on searching effectively. Poor representations or slow search techniques can make the difference between finding a good solution and finding no solution at all.
One example of constraint programming is scheduling: for instance, determining a schedule for a conference where there are 30 talks (that’s one constraint), only eight rooms to hold them in (that’s another constraint), and some talks can’t overlap (more constraints).
Every year, some of the world’s top constraint programming researchers compete for medals in the MiniZinc challenge. Problems range from scheduling to vehicle routing to program verification and frequency allocation.
Google’s open source solver, or-tools, took two gold medals and two silver medals. The gold medals were in parallel and portfolio search, and the silver medals were in fixed and free search. Google’s success was due in part to integrating a SAT solver to handle boolean constraints, and a new presolve phase inherited from integer programming.
Laurent Perron, a member of Google’s Optimization team and a lead contributor to or-tools, noted that every year brings fresh techniques to the competition: “One of the big surprises this year was the success of lazy-clause generation, which combines techniques from the SAT and constraint programming communities.”
If you’re interested in learning more about constraint programming, you can start at the wikipedia page, or have a look at or-tools.
The full list of winners is available here.
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