Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Narrowness, pathwidth, and their application in natural language processing
Discrete Applied Mathematics
A partial k-arboretum of graphs with bounded treewidth
Theoretical Computer Science
Tour Merging via Branch-Decomposition
INFORMS Journal on Computing
New lower and upper bounds for graph treewidth
WEA'03 Proceedings of the 2nd international conference on Experimental and efficient algorithms
A practical algorithm for finding optimal triangulations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Efficient approximation for triangulation of minimum treewidth
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Pre-processing for triangulation of probabilistic networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
SOFSEM'05 Proceedings of the 31st international conference on Theory and Practice of Computer Science
On the maximum cardinality search lower bound for treewidth
Discrete Applied Mathematics
On exact algorithms for treewidth
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Tree decomposition and discrete optimization problems: A survey
Cybernetics and Systems Analysis
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Treewidth computations I. Upper bounds
Information and Computation
Weighted treewidth algorithmic techniques and results
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Treewidth: characterizations, applications, and computations
WG'06 Proceedings of the 32nd international conference on Graph-Theoretic Concepts in Computer Science
On exact algorithms for treewidth
ACM Transactions on Algorithms (TALG)
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In this paper, we introduce and evaluate some heuristics to find an upper bound on the treewidth of a given graph. Each of the heuristics selects the vertices of the graph one by one, building an elimination list. The heuristics differ in the criteria used for selecting vertices. These criteria depend on the fill-in of a vertex and the related new notion of the fill-in-excluding-one-neighbor. In several cases, the new heuristics improve the bounds obtained by existing heuristics.