minizinc

What problem does it solve?

MiniZinc is used to formulate constraint satisfaction and combinatorial optimization problems so they can be solved reliably and efficiently instead of relying on ad-hoc, error-prone reasoning.

Core Features & Use Cases

• Expert MiniZinc Modeling Guidance: Provides a structured, advanced workflow for building tight, high-propagation models (tight domains, symmetry breaking, search annotations).
• Declarative Best Practices: Emphasizes global constraints, reusable primitives (predicates/functions), channeling techniques, and separation of model from instance data via parameters.
• Debugging and Verification Loop: Recommends a disciplined process for validating SAT/UNSAT outcomes with targeted tracing, invariants, and performance-focused refinement.

Common use cases include scheduling, assignment, grid/matrix constraint modeling, and building optimized combinatorial solvers where model quality strongly impacts runtime.

Quick Start

Ask the AI to produce a MiniZinc model (using globals.mzn) that solves your specified scheduling or assignment constraints with tight domains, symmetry breaking, and an output that presents human-readable decision variable values.