Bidimensionality and Geometric Graphs [chapter]

Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh
2012 Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms  
Bidimensionality theory was introduced by Demaine et al. [JACM 2005 ] as a framework to obtain algorithmic results for hard problems on minor closed graph classes. The theory has been sucessfully applied to yield subexponential time parameterized algorithms, EPTASs and linear kernels for many problems on families of graphs excluding a fixed graph H as a minor. In this paper we use several of the key ideas from Bidimensionality to give a new generic approach to design EPTASs and subexponential
more » ... me parameterized algorithms for problems on classes of graphs which are not minor closed, but instead exhibit a geometric structure. In particular we present EPTASs and subexponential time parameterized algorithms for FEEDBACK VERTEX SET, VERTEX COVER, CONNECTED VERTEX COVER, DIAMOND HITTING SET, on map graphs and unit disk graphs, and for CYCLE PACKING and MINIMUM-VERTEX FEEDBACK EDGE SET on unit disk graphs. To the best of our knowledge, these results were previously unknown, with the exception of the EPTAS and a subexponential time parameterized algorithm on unit disk graphs for VERTEX COVER, which were obtained by Marx [ESA 2005 ] and Alber and Fiala [J. Algorithms 2004 ], respectively. Our results are based on the recent decomposition theorems proved by Fomin et al. in [SODA 2011 ] and novel grid-excluding theorems in unit disc and map graphs without large cliques. Our algorithms work directly on the input graph and do not require the geometric representations of the input graph. We also show that our approach can not be extended in its full generality to more general classes of geometric graphs, such as intersection graphs of unit balls in R d , d ≥ 3. Specifically, we prove that FEEDBACK VERTEX SET on unit-ball graphs in R 3 neither admits PTASs unless P=NP, nor subexponential time algorithms unless the Exponential Time Hypothesis fails. Additionally, we show that the decomposition theorems which our approach is based on fail for disk graphs and that therefore any extension of our results to disk graphs would require new algorithmic ideas. On the other hand, we prove that our EPTASs and subexponential time algorithms for VERTEX COVER and CONNECTED VERTEX COVER carry over both to disk graphs and to unit-ball graphs in R d for every fixed d. Counting monadic second-order logic (CMSO) is monadic second-order logic (MSO) additionally equipped with an atomic formula card n,p (U ) for testing whether the cardinality of a set U is congruent to n modulo p, where n and p are integers independent of the input graph such that 0 ≤ n < p and p ≥ 2. We refer to [4, 14, 15] for a detailed introduction to CMSO. MIN-CMSO and MAX-CMSO problems are graph optimization problems where the objective is to find a maximum or minimum sized vertex or edge set satisfying a CMSO-expressible property. In particular, in a MIN/MAX-CMSO graph problem Π we are given a graph G as input. The objective is to find a minimum/maximum cardinality vertex/edge set S such that the CMSO-expressible predicate P Π (G, S) is satisfied. Bidimensionality and Separability. Our results concern graph optimization problems where the objective is to find a vertex or edge set that satisfies a feasibility constraint and maximizes or minimizes a problem-specific objective function. For a problem Π and vertex (edge) set S let φ Π (G, S) be the feasibility constraint returning true if S is feasible and false otherwise. Let κ Π (G, S) be the objective function. In most cases, κ Π (G, S) will return |S|. We will only consider problems where every instance has at least one feasible solution. Let U be the set of all graphs. For a graph optimization problem Π
doi:10.1137/1.9781611973099.124 dblp:conf/soda/FominLS12 fatcat:s7q42vkczjbp7drdztgo77ool4