Modeling and Programming with Gecode provides comprehensive documentation of how to model and program with Gecode.

The first part of the document explains modeling and solving constraint problems with Gecode. It explains how to program, compile, link, and execute these models. It provides an overview of integer, Boolean, and set variables and constraints, modeling support, search, and Gist. This is complemented by a collection of interesting case studies of how to model with Gecode.

The remaining, more advanced, parts are about programming with Gecode. They explain in great detail and with numerous examples the concepts and techniques for programming constraints, branchings, search engines, and new variable types with Gecode. The parts’ coverage puts users on par with Gecode’s developers.

This paper introduces propagator groups as an abstraction for controlling the execution of propagators as implementations of constraints. Propagator groups enable users of a constraint programming system to program how propagators within a group are executed.

The paper exemplifies propagator groups for controlling both propagation order and propagator interaction. Controlling propagation order is applied to debugging constraint propagation and optimal constraint propagation for Berge-acyclic propagator graphs. Controlling propagator interaction by encapsulating failure and entailment is applied to general reification and constructive disjunction. The paper describes an implementation of propagator groups (based on Gecode) that is applicable to any propagator-centered constraint programming system. Experiments show that groups incur little to no overhead and that the applications of groups are practically usable and efficient.

While incremental propagation for global constraints is recognized to be important, little research has been devoted to how propagator-centered constraint programming systems should support incremental propagation. This paper introduces advisors as a simple and efficient, yet widely applicable method for supporting incremental propagation in a propagator-centered setting. The paper presents how advisors can be used for achieving different forms of incrementality and evaluates cost and benefit for several global constraints.