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Tree-width and path-width are widely successful concepts. Many NP-hard problems have efficient solutions when restricted to graphs of bounded tree-width. Many efficient algorithms are based on a tree decomposition. Sometimes the more restricted path decomposition is required. The bottleneck for such algorithms is often the computation of the width and a corresponding tree or path decomposition. For graphs with $n$ vertices and tree-width or path-width $k$, the standard linear time algorithm to compute these decompositions dates back to 1996. Its running time is linear in $n$ and exponential in $k^3$ and not usable in practice. Here we present a more efficient algorithm to compute the path-width and provide a path decomposition. Its running time is $2^{O(k^2)} n$. In the classical algorithm of Bodlaender and Kloks, the path decomposition is computed from a tree decomposition. Here, an optimal path decomposition is computed from a path decomposition of about twice the width. The latter is computed from a constant factor smaller graph.
14 pages
FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)
FOS: Computer and information sciences, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 6 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |