Combining breadth-first and depth-first strategies in searching for treewidth

Authors:
Rong Zhou;Eric A. Hansen
Affiliations:
Palo Alto Research Center, Palo Alto, CA;Dept. of Computer Science and Engineering, Mississippi State University, MS
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 10
Cited 0

Complexity of finding embeddings in a k-tree

SIAM Journal on Algebraic and Discrete Methods
Bucket elimination: a unifying framework for reasoning

Artificial Intelligence
Recursive conditioning

Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Sweep A*: Space-Efficient Heuristic Search in Partially Ordered Graphs

ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A complete anytime algorithm for treewidth

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Frontier search

Journal of the ACM (JACM)
Breadth-first heuristic search

Artificial Intelligence
On exact algorithms for treewidth

ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Structured duplicate detection in external-memory graph search

AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Best-first search for treewidth

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

Breadth-first and depth-first search are basic search strategies upon which many other search algorithms are built. In this paper, we describe an approach to integrating these two strategies in a single algorithm that combines the complementary strengths of both. We show the benefits of this approach using the treewidth problem as an example.