TGtgamblin·May 26, 2023
Love this article and the push to build awareness of what modern SAT solvers can do.
It's worth mentioning that there are higher level abstractions that are far more accessible than SAT. If I were teaching a course on this, I would start with either Answer Set Programming (ASP) or Satisfiability Modulo Theories (SMT). The most widely used solvers for those are clingo [0] and Z3 [1]:
With ASP, you write in a much clearer Prolog-like syntax that does not require nearly as much encoding effort as your typical SAT problem. Z3 is similar -- you can code up problems in a simple Python API, or write them in the smtlib language.
Both of these make it easy to add various types of optimization, constraints, etc. to your problem, and they're much better as modeling languages than straight SAT. Underneath, they have solvers that leverage all the
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modern CDCL tricks.
We wrote up a paper [2] on how to formulate a modern dependency solver in ASP; it's helped tremendously for adding new types of features like options, variants, and complex compiler/arch dependencies to Spack [3]. You could not get good solutions to some of these problems without a capable and expressive solver.
[0] https://github.com/potassco/clingo
[1] https://github.com/Z3Prover/z3
[2] https://arxiv.org/abs/2210.08404, https://dl.acm.org/doi/abs/10.5555/3571885.3571931
[3] https://github.com/spack/spack