Berthe Y. Choueiry
Date of this Version
Daniel J. Geschwender. Effectively Enforcing Minimality During Backtrack Search. MS thesis, University of Nebraska-Lincoln, 2018.
Constraint Processing is an expressive and powerful framework for modeling and solving combinatorial decision problems. Enforcing consistency during backtrack search is an effective technique for reducing thrashing in a large search tree. The higher the level of the consistency enforced, the stronger the pruning of inconsistent subtrees. Recently, high-level consistencies (HLC) were shown to be instrumental for solving difficult instances. In particular, minimality, which is guaranteed to prune all inconsistent branches, is advantageous even when enforced locally. In this thesis, we study two algorithms for computing minimality and propose three new mechanisms that significantly improve performance. Then, we integrate the resulting algorithms in a portfolio that operates both locally and dynamically during search. Finally, we empirically evaluate the performance of our approach on benchmark problems.
Adviser: Berthe Y. Choueiry