This is a tough comparison. They are very similar in some respects but also very distinct. Prolog has some operational flavor to it. You can predict what it'll

This is a tough comparison. They are very similar in some respects but also very distinct. Prolog has some operational flavor to it. You can predict what it'll do. It can be used as "just" a programming language akin to python with fairly little logical/mathemtical content. Declarative Prolog as in constraint logic programming is in the realm of attacking similar logical problems to z3. scipy.optimize is a grab bag of stuff. Some of it is pretty heuristic. It's all subject to floating point error, which is the devil we live with. The milp and lp solvers are relatives of how z3 handles linear inequality constraints. scipy.optimize doesn't treat logic at all really. It takes in mathematical multivariate functions and finds roots or minima. Z3 is intended to be mathematically rigorous. It is less operational than prolog. On big combinatorial problems (SAT, bitvector problems), it is probably a superior tool. It does have an optimization interface, I haven't used it much.