Yes, the underlying engine of Picat is - a slightly altered- B-Prolog, which is available as the "bp" module, from which one can use many of traditional Prolog constructs, for example `bp.length/2` instead of Picat's `length/1` function. This can help when porting Prolog programs to Picat. In fact, quite a few Prolog programs can be run directly in Picat, perhaps with just some few adjustment.
I really like MiniZinc, especially that one can test a lot of different type of solvers for a problem.
But one of its drawbacks is its limitation of handling input and output (including preprocessing and postprocessing). In some cases - for example when the output is rather simple - I use for example Picat/Python to transform input to MiniZinc format (.dzn format or JSON) and then run MiniZinc.
But for more fancy output I tend to use Picat or Python + (OR-tools CP/SAT or CPMpy or minizinc-python).
Constraint programming solvers tends to focus on finite domain (integers) but there are some solvers that has some supports for floats as decision variables. You can try some of these solvers.
For non linear models:
* Gecode (included in the MiniZincIDE distribtion)
I haven't seen a comparison on LP-focused benchmarks with OR-tools CP-SAT. In the 2022 MiniZinc Challenge (https://www.minizinc.org/challenge2022/results2022.html ) both Gurobi, CPLEX (as well and SCIP and HiGHS) participated: CP-SAT was the best in all the categories it participated in.
The CP-SAT solver is architectured around five components:
The base layer is a clause learning SAT solver.
Above the SAT layer sits a Constraint Programming (CP) module with Boolean, integer and interval variables, and standard integer, scheduling and routing constraints.
Alongside the CP solver, a simplex provides a global linear relaxation. Its integration with the CP and SAT layers enable the CP-SAT solver to solve MIP problems with the same techniques as (commercial) MIP solvers: relaxation, cuts, heuristics and duality based techniques.
Both the CP and MIP modules rely on a unified protobuf representation of the model that can serve as a file format, as well as an intermediate representation of the model during all phases of the solve (input format, presolved model, LNS fragment).
On top, the search layer implements a robust information-sharing portfolio of specialized workers that offers both good and fast solutions, and superior optimality proving capabilities.
One interesting development in the latter years is the mix of Constraint programming and SAT solvers. For example Google OR-tools (https://developers.google.com/optimization/ ) which mixes CP, SAT and MIP. It has won most of the MiniZinc Challenge categories (https://www.minizinc.org/challenge.html ) several years now. The Chuffed solver is another solver mixing CP and SAT.
For some example of the available predicates in the bp module, see my http://hakank.org/picat/v3_utils.pi . Also, see http://hakank.org/picat/#v3 for some examples of ported Prolog programs.