TL;DR
An account of the design and implementation of OpenSolver, an abstract branch-and-propagate tree search engine that supports a wide range of relevant solver configurations, and of many experiments that were performed to evaluate the approach.
This paper describes how to build a (bounds consistent) global propagator for difference constraints that treats them all simultaneously, and shows how to explain propagations by a global difference constraint propagator, in order to use it within a lazy clause generation solver.
Combinatorial problems appear in numerous industrial applications. A common approach is to formulate these problems as declarative constraint models that can subsequently be compiled to and solved by a range of back-end solvers. Recent work shows that Large Language Models (LLMs) can produce correct models from natural...
A set of reformulation rules are proposed that allow the use of half-reification of a global constraint with any CP solver that supports the “normal” global constraint propagator, and expand the range of available solvers and constraint models that can be used in XCP techniques or for solving CSPs with compound constra...
This work presents a pipeline that combines LLM-based constraint generation with empirical evaluation and formal verification, and handles MiniZinc’s partial semantics by requiring the base model to be safe and separately proving that the proposed constraint is well-defined for all instances and solutions of the base m...
This work presents a tool that implements data-driven approaches that can dynamically select a smaller number of instances that provide sufficient statistical evidence to evaluate the relative performance of a given set of solvers and makes them readily accessible to solver developers, thus enabling them to obtain swif...
This work introduces an agentic framework that reformulates a constraint model from an open-ended space and establishes correctness empirically rather than by construction, and demonstrates that autonomous agentic methods can support the improvement of constraint models.
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