TY - GEN
T1 - Faster Parameterized Algorithms for Modification Problems to Minor-Closed Classes
AU - Morelle, Laure
AU - Sau, Ignasi
AU - Stamoulis, Giannos
AU - Thilikos, Dimitrios M.
N1 - Publisher Copyright:
© Laure Morelle, Ignasi Sau, Giannos Stamoulis, and Dimitrios M. Thilikos.
PY - 2023/7
Y1 - 2023/7
N2 - Let G be a minor-closed graph class and let G be an n-vertex graph. We say that G is a k-apex of G if G contains a set S of at most k vertices such that G \ S belongs to G. Our first result is an algorithm that decides whether G is a k-apex of G in time 2poly(k) · n2. This algorithm improves the previous one, given by Sau, Stamoulis, and Thilikos [ICALP 2020, TALG 2022], whose running time was 2poly(k) · n3. The elimination distance of G to G, denoted by edG(G), is the minimum number of rounds required to reduce each connected component of G to a graph in G by removing one vertex from each connected component in each round. Bulian and Dawar [Algorithmica 2017] proved the existence of an FPT-algorithm, with parameter k, to decide whether edG(G) ≤ k. This algorithm is based on the computability of the minor-obstructions and its dependence on k is not explicit. We extend the techniques used in the first algorithm to decide whether edG(G) ≤ k in time 222poly(k) · n2. This is the first algorithm for this problem with an explicit parametric dependence in k. In the special case where G excludes some apex-graph as a minor, we give two alternative algorithms, one running in time 22O(k2 log k) · n2 and one running in time 2poly(k) · n3. As a stepping stone for these algorithms, we provide an algorithm that decides whether edG(G) ≤ k in time 2O(tw·k+tw log tw) · n, where tw is the treewidth of G. This algorithm combines the dynamic programming framework of Reidl, Rossmanith, Villaamil, and Sikdar [ICALP 2014] for the particular case where G contains only the empty graph (i.e., for treedepth) with the representative-based techniques introduced by Baste, Sau, and Thilikos [SODA 2020]. In all the algorithmic complexities above, poly is a polynomial function whose degree depends on G, while the hidden constants also depend on G. Finally, we provide explicit upper bounds on the size of the graphs in the minor-obstruction set of the class of graphs Ek(G) = {G | edG(G) ≤ k}.
AB - Let G be a minor-closed graph class and let G be an n-vertex graph. We say that G is a k-apex of G if G contains a set S of at most k vertices such that G \ S belongs to G. Our first result is an algorithm that decides whether G is a k-apex of G in time 2poly(k) · n2. This algorithm improves the previous one, given by Sau, Stamoulis, and Thilikos [ICALP 2020, TALG 2022], whose running time was 2poly(k) · n3. The elimination distance of G to G, denoted by edG(G), is the minimum number of rounds required to reduce each connected component of G to a graph in G by removing one vertex from each connected component in each round. Bulian and Dawar [Algorithmica 2017] proved the existence of an FPT-algorithm, with parameter k, to decide whether edG(G) ≤ k. This algorithm is based on the computability of the minor-obstructions and its dependence on k is not explicit. We extend the techniques used in the first algorithm to decide whether edG(G) ≤ k in time 222poly(k) · n2. This is the first algorithm for this problem with an explicit parametric dependence in k. In the special case where G excludes some apex-graph as a minor, we give two alternative algorithms, one running in time 22O(k2 log k) · n2 and one running in time 2poly(k) · n3. As a stepping stone for these algorithms, we provide an algorithm that decides whether edG(G) ≤ k in time 2O(tw·k+tw log tw) · n, where tw is the treewidth of G. This algorithm combines the dynamic programming framework of Reidl, Rossmanith, Villaamil, and Sikdar [ICALP 2014] for the particular case where G contains only the empty graph (i.e., for treedepth) with the representative-based techniques introduced by Baste, Sau, and Thilikos [SODA 2020]. In all the algorithmic complexities above, poly is a polynomial function whose degree depends on G, while the hidden constants also depend on G. Finally, we provide explicit upper bounds on the size of the graphs in the minor-obstruction set of the class of graphs Ek(G) = {G | edG(G) ≤ k}.
KW - Elimination distance
KW - Flat Wall Theorem
KW - Graph minors
KW - Graph modification problems
KW - Irrelevant vertex technique
KW - Obstructions
KW - Parameterized algorithms
KW - Vertex deletion
UR - http://www.scopus.com/inward/record.url?scp=85167365938&partnerID=8YFLogxK
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U2 - 10.4230/LIPIcs.ICALP.2023.93
DO - 10.4230/LIPIcs.ICALP.2023.93
M3 - Conference contribution
AN - SCOPUS:85167365938
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 50th International Colloquium on Automata, Languages, and Programming, ICALP 2023
A2 - Etessami, Kousha
A2 - Feige, Uriel
A2 - Puppis, Gabriele
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 50th International Colloquium on Automata, Languages, and Programming, ICALP 2023
Y2 - 10 July 2023 through 14 July 2023
ER -