Eiben, E., Ganian, R., Hamm, T., Jaffke, L., & Kwon, O.-J. (2022). A Unifying Framework for Characterizing and Computing Width Measures. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022) (pp. 1–23). Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH. https://doi.org/10.4230/LIPIcs.ITCS.2022.63
Algorithms for computing or approximating optimal decompositions for decompositional parameters such as treewidth or clique-width have so far traditionally been tailored to specific width parameters. Moreover, for mim-width, no efficient algorithms for computing good decompositions were known, even under highly restrictive parameterizations. In this work we identify F-branchwidth as a class of generic decompositional parameters that can capture mim-width, treewidth, clique-width as well as other measures. We show that while there is an infinite number of F-branchwidth parameters, only a handful of these are asymptotically distinct. We then develop fixed-parameter and kernelization algorithms (under several structural parameterizations) that can approximate every possible F-branchwidth, providing a unifying parameterized framework that can efficiently obtain near-optimal tree-decompositions, k-expressions, as well as optimal mim-width decompositions.
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Project title:
Parameterisierte Analyse in der Künstlichen Intelligenz: Y1329-N (Fonds zur Förderung der wissenschaftlichen Forschung (FWF))
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Project (external):
Austrian Science Fund (FWF) Austrian Science Fund (FWF) Norwegian Research Council (NFR) National Research Foundation of Korea (NRF) Institute for Basic Science