Ok, we'll put that year above instead. Thanks!

The amusing ones, to me, are the people that know of the techniques, but are convinced they can't apply.

Obviously not everything will be easy to map into a classic optimization problem. And you may have a heuristic approach that is better for a problem. But the general solvers out there have gone a long long way.

Oh yeah, there are whole subfields of engineering that the current crop of AI deep learning engineers are mostly unfamiliar with. I've been able to find places where I can make significant advances on the state of the art in AI through incorporation of concepts from decision theory, control theory, process engineering, constraint optimization, etc.

Many use Z3 for type checking like F*, Liquid Haskell, Dafny.

I know about only one such library, and works great for toy problems: PuLP [0][1].

[0]: https://coin-or.github.io/pulp/

[1]: https://pypi.org/project/PuLP/

The state of the art solvers are the proprietary ones like Gurobi, FICO, Cplex, Mosek, etc. A major contributor to the proprietary "sauce" is in the heuristics they use. For example, all solvers will have a "presolve" phase which attempts to eliminate redundant constraints/variables. There may be some ML they are using behind the scenes to derive these heuristics, I'm not sure, although I know it is a major research area.

Otherwise, the basic underlying algorithms are all the same, as in the textbook: branch-and-bound and so on.