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SAT solvers

Cons

  • Require input problem to be a propositional logic formula in conjunctive normal form (CNF). This is not a natural way to express most problems that require SAT

  • Computing CNF formulas is often bad and hard so SAT solvers aren't really at the right "level" for use by the working programmer

  • Look up cardinality constraints CNF on google scholar - reveals lots of problems and tradeoffs that can be made

Why SMT over SAT?

  • SMT solvers allow more freedom in the expression of input problems - support integers, fixed width floats, arrays and potentially other datatypes, as well as common operations on those types, without requiring a specific normal form!

  • API that allows for the manipulation of the input formula exposed by the solver, unlike strict

How do they work?

Bit blasting

  • Directly convert input formula into an equivalent Boolean formula in CNF

  • Limited to formulas where every data type has a finite set of values

  • Need a SAT solver as a backend, any improvement to SAT translates directly to an improvement to an SMT solver - so this is just additional tooling around a SAT solver to make it much easier to use.

CDCL(T)

  • Definition: conflict driven cause learning - the algorithm employed by most modern SAT solvers.

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