Realtime Generation of Streamliners with Large Language Models

Streamliners are certain constraints added to a constraint model to reduce the search space, thereby improving the feasibility and speed of finding solutions to complex constraint satisfaction problems. By incorporating domain-specific knowledge, streamliners can guide the constraint solver, allowing it to bypass less promising areas of the search space. Gomes and Sellmann (2004a) introduced streamliners to speed up the constrained-based search for hard combinatorial design problems. Today, streamliners are a standard tool for speeding up constrained-based search. Streamliners are closely related to implied/redundant constraints, symmetry-breaking constraints, and dominance-breaking constraints; however, adding a streamliner may even cause the constraint model to become inconsistent. Originally, streamliners were hand-crafted by researchers who used their theoretical insight to analyze the constrained model. However, progress has also been made on the automated generation of streamliners (Spracklen et al. 2023) by systematically trying the effect of some atomic constraints, such as imposing specific constraints on integer and function domains, like enforcing odd or even values, monotonicity, and properties like commutativity, as well as facilitating specific attributes in binary relations. These atomic restrictions are tested on thousands of problem instances, and those that show a good streamlining effect are systematically combined.