Choco

Choco, short for Choco Solver, was created by Charles Prud'homme and colleagues in 2005. Choco is a Java library for constraint satisfaction problems (CSP), combinatorial optimization, and scheduling. It is primarily used in academic research, industrial scheduling, and solving optimization problems in software that requires constraint programming. Developers can access Choco through the official Choco GitHub repository, which provides the library, examples, and documentation for Windows, macOS, and Linux platforms.

Choco exists to provide a declarative framework for expressing and solving complex constraints efficiently. Its design philosophy emphasizes modularity, flexibility, and performance. By offering constraint variables, propagators, and search strategies, Choco allows developers to model complex problems without implementing low-level algorithms, solving scheduling, allocation, and optimization tasks more intuitively.

Choco: Variables and Domains

Choco programs define variables with finite domains, representing possible values that a variable can take.

import org.chocosolver.solver.Model;
import org.chocosolver.solver.variables.IntVar;

Model model = new Model("Simple CSP");
IntVar X = model.intVar("X", 1, 10);
IntVar Y = model.intVar("Y", 1, 10);

Variables encapsulate possible values, and domains define constraints on assignment. This is conceptually similar to MiniZinc and ECLiPSe.

Choco: Constraints

Choco allows developers to specify constraints declaratively, such as arithmetic, logical, or global constraints.

model.arithm(X, "+", Y, "=", 10).post();

Constraints restrict variable assignments, guiding the solver toward valid solutions. Declarative constraints simplify complex problem modeling without implementing search logic manually, similar to MiniZinc.

Choco: Search and Solving

Choco uses search strategies and propagation to find solutions to the constraint model efficiently.

model.getSolver().solve();
System.out.println("X: " + X.getValue() + ", Y: " + Y.getValue());

The solver automatically explores the search space using backtracking and propagation techniques. This approach aligns with constraint solvers in MiniZinc and ECLiPSe.

Choco: Optimization

Choco can handle optimization problems by defining objective functions to maximize or minimize.

IntVar Z = model.intVar("Z", 0, 100);
model.setObjective(Model.MINIMIZE, Z);

Optimization guides the solver toward the best solution according to defined criteria. This feature is commonly used in scheduling, resource allocation, and combinatorial problems, conceptually similar to MiniZinc and ECLiPSe.

Choco is used in research, industrial scheduling, and constraint-based modeling, providing a powerful, modular Java library for defining and solving complex problems. When combined with MiniZinc, ECLiPSe, and Prolog, Choco enables developers to express constraints declaratively, solve optimization problems efficiently, and build scalable, maintainable solutions for complex applications.