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Black-Box Value Heuristics for Solving Optimization Problems with Constraint Programming (Short Paper)

Authors Augustin Delecluse , Pierre Schaus

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LIPIcs.CP.2024.36.pdf
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Author Details

Augustin Delecluse
  • TRAIL, ICTEAM, UCLouvain, Belgium
Pierre Schaus
  • ICTEAM, UCLouvain, Belgium

Funding

  • Delecluse, Augustin: This work was supported by Service Public de Wallonie Recherche under grant n°2010235 – ARIAC by DIGITALWALLONIA4.A.

Cite AsGet BibTex

Augustin Delecluse and Pierre Schaus. Black-Box Value Heuristics for Solving Optimization Problems with Constraint Programming (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 36:1-36:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.36

Abstract

Significant research efforts have focused on black-box variable selection, with less attention given to value heuristics. An ideal value heuristic enables depth-first-search to prioritize high-quality solutions first. The Bound-Impact Value Selection achieves this goal through a look-ahead strategy, trying every value of the selected variable and ranking them based on their impact on the objective. However, this method is generally too computationally intensive for the entire search tree. We introduce two simple yet powerful modifications to improve its scalability. First, a lighter fix point computation involving only the constraints on the shortest path in the constraint graph between the variable and the objective. Second, a reverse look-ahead strategy optimistically fixes the objective variable to its minimum in order to prioritize the remaining values. These two ideas have been empirically validated on a range of academic problems and in the XCSP³ competition, demonstrating significant improvements in scalability.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
Keywords
  • Constraint Programming
  • Value Selection
  • Look-Ahead
  • Optimization

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Supplementary Materials

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