2ima00

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2ima00 [2016/05/26 12:25] bmpjansen [Student lectures] |
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I don't know which of the 2 approaches will be faster on the inputs we have; one has a worse polynomial term, the other has a worse factor f(k). I don't think it is feasible to implement both, given the time. Pick one of the two and go for it. To **find** a tree decomposition, you can use one of the heuristics described in this paper: | I don't know which of the 2 approaches will be faster on the inputs we have; one has a worse polynomial term, the other has a worse factor f(k). I don't think it is feasible to implement both, given the time. Pick one of the two and go for it. To **find** a tree decomposition, you can use one of the heuristics described in this paper: | ||

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[[http://dx.doi.org/10.1016/j.ic.2009.03.008|Hans L. Bodlaender, Arie M. C. A. Koster: | [[http://dx.doi.org/10.1016/j.ic.2009.03.008|Hans L. Bodlaender, Arie M. C. A. Koster: | ||

Treewidth computations I. Upper bounds. Inf. Comput. 208(3): 259-275 (2010)]] | Treewidth computations I. Upper bounds. Inf. Comput. 208(3): 259-275 (2010)]] | ||

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+ | Within the framework of iterative compression and you have *some* feedback vertex set X in your graph G, you can also make a tree decomposition by making a tree decomposition for the forest G - X (which is easy) and then adding all vertices of X to all bags. Afterwards, you can try to make it better by removing vertices from X from bags when the tree decomposition remains valid after removing a vertex from a bag. | ||

**Parameterized by solution size (iterative compression & randomized): Huib & Stefan.** | **Parameterized by solution size (iterative compression & randomized): Huib & Stefan.** |

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