Abstract
We present a method to detect implicit model patterns (such as global constraints) that might be able to replace parts of a combinatorial problem model that are expressed at a low-level. This can help non-expert users write higher-level models that are easier to reason about and often yield better performance. Our method generates candidate model patterns by analyzing both the structure of the model – its constraints, variables, parameters and loops – and the input data from one or more data files. Each candidate is scored by comparing a sample of its solution space with that of the part of the model it is intended to replace. The top-scoring candidates are presented to the user through an interactive display, which shows how they could be incorporated into the model. The method is implemented for the MiniZinc modeling language and available as part of the MiniZinc distribution.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.