Synthesis of Constraint-Based Local Search Algorithms from High-Level Models
Abstract
The gap in automation between MIP/SAT solvers and those for constraint programming and constraint-based local search hinders experimentation and adoption of these technologies and slows down scientific progress. This paper addresses this important issue: It shows how effective local search procedures can be automatically synthesized from models expressed in a rich constraint language. The synthesizer analyzes the model and derives the local search algorithm for a specific meta-heuristic by exploiting the structure of the model and the constraint semantics. Experimental results suggest that the synthesized procedures only induce a small loss in efficiency on a variety of realistic applications in sequencing, resource allocation, and facility location.
Cite
Text
Van Hentenryck and Michel. "Synthesis of Constraint-Based Local Search Algorithms from High-Level Models." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Van Hentenryck and Michel. "Synthesis of Constraint-Based Local Search Algorithms from High-Level Models." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/hentenryck2007aaai-synthesis/)BibTeX
@inproceedings{hentenryck2007aaai-synthesis,
title = {{Synthesis of Constraint-Based Local Search Algorithms from High-Level Models}},
author = {Van Hentenryck, Pascal and Michel, Laurent D.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {273-279},
url = {https://mlanthology.org/aaai/2007/hentenryck2007aaai-synthesis/}
}