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@article{1523846, author = {Ganian, Robert and Ordyniak, Sebastian}, article_number = {1}, doi = {http://dx.doi.org/10.1016/j.artint.2017.12.006}, keywords = {integer linear programming; treewidth; treedepth; parameterized complexity}, language = {eng}, issn = {0004-3702}, journal = {ARTIFICIAL INTELLIGENCE}, title = {The complexity landscape of decompositional parameters for ILP}, volume = {257}, year = {2018} }
TY - JOUR ID - 1523846 AU - Ganian, Robert - Ordyniak, Sebastian PY - 2018 TI - The complexity landscape of decompositional parameters for ILP JF - ARTIFICIAL INTELLIGENCE VL - 257 IS - 1 SP - 61-71 EP - 61-71 PB - Elsevier Science SN - 00043702 KW - integer linear programming KW - treewidth KW - treedepth KW - parameterized complexity N2 - Integer Linear Programming (ILP) can be seen as the archetypical problem for NP-complete optimization problems, and a wide range of problems in artificial intelligence are solved in practice via a translation to ILP. Despite its huge range of applications, only few tractable fragments of ILP are known, probably the most prominent of which is based on the notion of total unimodularity. Using entirely different techniques, we identify new tractable fragments of ILP by studying structural parameterizations of the constraint matrix within the framework of parameterized complexity. In particular, we show that ILP is fixed-parameter tractable when parameterized by the treedepth of the constraint matrix and the maximum absolute value of any coefficient occurring in the ILP instance. Together with matching hardness results for the more general parameter treewidth, we give an overview of the complexity of ILP w.r.t. decompositional parameters defined on the constraint matrix. ER -
GANIAN, Robert and Sebastian ORDYNIAK. The complexity landscape of decompositional parameters for ILP. \textit{ARTIFICIAL INTELLIGENCE}. Elsevier Science, 2018, vol.~257, No~1, p.~61-71. ISSN~0004-3702. Available from: https://dx.doi.org/10.1016/j.artint.2017.12.006.
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