Improving combinatorial optimization
Year: 2011
Type: dissertation
Abstract: Combinatorial Optimization is an important area of computer science that has many theoretical and practical applications.In this thesis, we present important contributions to several different areas of combinatorial optimization, including nogood learning, symmetry breaking, dominance, relaxations and parallelization.We develop a new nogood learning technique based on constraint projection that allows us to exploit subproblem dominances that arise when two different search paths lead to subproblems which are identical on the remaining unlabeled variables.On appropriate problems, this nogood learning technique provides orders of magnitude speedup compared to a base solver which does not learn nogoods. We present a new symmetry breaking technique called SBDS-1UIP, which is an extension of Symmetry Breaking During Search (SBDS). SBDS-1UIP uses symmetric
Author: Geoffrey Chu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2278850436