MiniZinc, short for MiniZinc Modeling Language, was created by Christian Schulte and collaborators in 2005. MiniZinc is a high-level, declarative modeling language for constraint satisfaction problems (CSP), combinatorial optimization, and scheduling. It is used for academic research, industrial optimization, and prototyping complex constraint-based models. Developers can access MiniZinc through the official site: MiniZinc Official Downloads, which provides the compiler, solver integrations, examples, and documentation for Windows, macOS, and Linux platforms.
MiniZinc exists to simplify the modeling of complex constraint problems while remaining solver-independent. Its design philosophy emphasizes readability, modularity, and expressiveness. By separating model definition from solver implementation, MiniZinc allows developers to focus on the declarative description of problems, while enabling flexible experimentation with different constraint solvers. This approach solves the problem of tying models to specific solvers and provides a clear, maintainable framework for optimization and scheduling applications.
MiniZinc: Variables and Domains
MiniZinc defines variables with explicit domains that restrict their possible values.
var 1..10: X;
var 1..10: Y;
constraint X + Y = 10;Variables encapsulate potential values, and constraints define relationships among them. This declarative approach is conceptually similar to ECLiPSe and Choco.
MiniZinc: Constraints and Predicates
MiniZinc supports arithmetic, logical, and global constraints. Predicates encapsulate reusable logic for modularity.
predicate sum_is_ten(var int: A, var int: B) =
A + B = 10;
constraint sum_is_ten(X, Y);Constraints restrict variable assignments, and predicates improve readability and reusability. This approach allows for clear problem modeling, similar to Choco.
MiniZinc: Search and Solving
MiniZinc delegates solving to underlying solvers, using search strategies to explore the solution space efficiently.
solve satisfy;
output ["X = ", show(X), ", Y = ", show(Y)];The solver automatically applies backtracking and constraint propagation to find feasible solutions. This process is analogous to constraint solving in ECLiPSe and Choco.
MiniZinc: Optimization
MiniZinc supports optimization by defining an objective function to maximize or minimize.
var 0..100: Z;
solve minimize Z;The objective guides the solver toward the best solution according to defined criteria. This enables scheduling, resource allocation, and combinatorial problem-solving, conceptually similar to Choco and ECLiPSe.
MiniZinc is used for research, industrial scheduling, resource allocation, and education. Its solver-independent, declarative design makes it ideal for expressing constraints clearly and experimenting with multiple solvers. When used alongside ECLiPSe, Choco, and Prolog, MiniZinc provides a powerful environment for modeling, analyzing, and solving complex constraint-based problems efficiently and maintainably.