11 research outputs found
Minimal disconnected cuts in planar graphs
The problem of finding a disconnected cut in a graph is NP-hard in general but polynomial-time solvable on planar graphs. The problem of finding a minimal disconnected cut is also NP-hard but its computational complexity is not known for planar graphs. We show that it is polynomial-time solvable on 3-connected planar graphs but NP-hard for 2-connected planar graphs. Our technique for the first result is based on a structural characterization of minimal disconnected cuts in 3-connected K 3,3 -free-minor graphs and on solving a topological minor problem in the dual. We show that the latter problem can be solved in polynomial-time even on general graphs. In addition we show that the problem of finding a minimal connected cut of size at least 3 is NP-hard for 2-connected apex graphs
Matching cutsets in graphs of diameter 2
AbstractWe say that a graph has a matching cutset if its vertices can be coloured in red and blue in such a way that there exists at least one vertex coloured in red and at least one vertex coloured in blue, and every vertex has at most one neighbour coloured in the opposite colour. In this paper we study the algorithmic complexity of a problem of recognizing graphs which possess a matching cutset. In particular we present a polynomial-time algorithm which solves this problem for graphs of diameter two
Minimal Disconnected Cuts in Planar Graphs
The problem of finding a disconnected cut in a graph is NP-hard in general but polynomial-time solvable on planar graphs. The problem of finding a minimal disconnected cut is also NP-hard but its computational complexity was not known for planar graphs. We show that it is polynomial-time solvable on 3-connected planar graphs but NP-hard for 2-connected planar graphs. Our technique for the first result is based on a structural characterization of minimal disconnected cuts in 3-connected inline image-free-minor graphs and on solving a topological minor problem in the dual. In addition we show that the problem of finding a minimal connected cut of size at least 3 is NP-hard for 2-connected apex graphs. Finally, we relax the notion of minimality and prove that the problem of finding a so-called semi-minimal disconnected cut is still polynomial-time solvable on planar graphs
Separability and Vertex Ordering of Graphs
Many graph optimization problems, such as finding an optimal coloring, or a largest clique, can be solved by a divide-and-conquer approach. One such well-known technique is decomposition by clique separators where a graph is decomposed into special induced subgraphs along their clique separators. While the most common practice of this method employs minimal clique separators, in this work we study other variations as well. We strive to characterize their structure and in particular the bound on the number of atoms. In fact, we strengthen the known bounds for the general clique cutset decomposition and the minimal clique separator decomposition. Graph ordering is the arrangement of a graph’s vertices according to a certain logic and is a useful tool in optimization problems. Special types of vertices are often recognized in graph classes, for instance it is well-known every chordal graph contains a simplicial vertex. Vertex-ordering, based on such properties, have originated many linear time algorithms. We propose to define a new family named SE-Class such that every graph belonging to this family inherently contains a simplicial extreme, that is a vertex which is either simplicial or has exactly two neighbors which are non-adjacent. Our family lends itself to an ordering based on simplicial extreme vertices (named SEO) which we demonstrate to be advantageous for the coloring and maximum clique problems. In addition, we examine the relation of SE-Class to the family of (Even-Hole, Kite)-free graphs and show a linear time generation of SEO for (Even-Hole, Diamond, Claw)-free graphs. We showcase the applications of those two core tools, namely clique-based decomposition and vertex ordering, on the (Even-Hole, Kite)-free family
Parameterized Algorithms for Graph Partitioning Problems
In parameterized complexity, a problem instance (I, k) consists of an input I and an
extra parameter k. The parameter k usually a positive integer indicating the size of the
solution or the structure of the input. A computational problem is called fixed-parameter
tractable (FPT) if there is an algorithm for the problem with time complexity O(f(k).nc
),
where f(k) is a function dependent only on the input parameter k, n is the size of the
input and c is a constant. The existence of such an algorithm means that the problem
is tractable for fixed values of the parameter. In this thesis, we provide parameterized
algorithms for the following NP-hard graph partitioning problems:
(i) Matching Cut Problem: In an undirected graph, a matching cut is a partition
of vertices into two non-empty sets such that the edges across the sets induce a matching.
The matching cut problem is the problem of deciding whether a given graph has
a matching cut. The Matching Cut problem is expressible in monadic second-order
logic (MSOL). The MSOL formulation, together with Courcelle’s theorem implies linear
time solvability on graphs with bounded tree-width. However, this approach leads to a
running time of f(||ϕ||, t) · n, where ||ϕ|| is the length of the MSOL formula, t is the
tree-width of the graph and n is the number of vertices of the graph. The dependency of
f(||ϕ||, t) on ||ϕ|| can be as bad as a tower of exponentials.
In this thesis we give a single exponential algorithm for the Matching Cut problem
with tree-width alone as the parameter. The running time of the algorithm is 2O(t)
· n.
This answers an open question posed by Kratsch and Le [Theoretical Computer Science,
2016]. We also show the fixed parameter tractability of the Matching Cut problem
when parameterized by neighborhood diversity or other structural parameters.
(ii) H-Free Coloring Problems: In an undirected graph G for a fixed graph H,
the H-Free q-Coloring problem asks to color the vertices of the graph G using at
most q colors such that none of the color classes contain H as an induced subgraph.
That is every color class is H-free. This is a generalization of the classical q-Coloring
problem, which is to color the vertices of the graph using at most q colors such that no
pair of adjacent vertices are of the same color. The H-Free Chromatic Number is
the minimum number of colors required to H-free color the graph.
For a fixed q, the H-Free q-Coloring problem is expressible in monadic secondorder
logic (MSOL). The MSOL formulation leads to an algorithm with time complexity
f(||ϕ||, t) · n, where ||ϕ|| is the length of the MSOL formula, t is the tree-width of the
graph and n is the number of vertices of the graph.
In this thesis we present the following explicit combinatorial algorithms for H-Free
Coloring problems:
• An O(q
O(t
r
)
· n) time algorithm for the general H-Free q-Coloring problem,
where r = |V (H)|.
• An O(2t+r log t
· n) time algorithm for Kr-Free 2-Coloring problem, where Kr is
a complete graph on r vertices.
The above implies an O(t
O(t
r
)
· n log t) time algorithm to compute the H-Free Chromatic
Number for graphs with tree-width at most t. Therefore H-Free Chromatic
Number is FPT with respect to tree-width.
We also address a variant of H-Free q-Coloring problem which we call H-(Subgraph)Free
q-Coloring problem, which is to color the vertices of the graph such that none of the
color classes contain H as a subgraph (need not be induced).
We present the following algorithms for H-(Subgraph)Free q-Coloring problems.
• An O(q
O(t
r
)
· n) time algorithm for the general H-(Subgraph)Free q-Coloring
problem, which leads to an O(t
O(t
r
)
· n log t) time algorithm to compute the H-
(Subgraph)Free Chromatic Number for graphs with tree-width at most t.
• An O(2O(t
2
)
· n) time algorithm for C4-(Subgraph)Free 2-Coloring, where C4
is a cycle on 4 vertices.
• An O(2O(t
r−2
)
· n) time algorithm for {Kr\e}-(Subgraph)Free 2-Coloring,
where Kr\e is a graph obtained by removing an edge from Kr.
• An O(2O((tr2
)
r−2
)
· n) time algorithm for Cr-(Subgraph)Free 2-Coloring problem,
where Cr is a cycle of length r.
(iii) Happy Coloring Problems: In a vertex-colored graph, an edge is happy if its
endpoints have the same color. Similarly, a vertex is happy if all its incident edges are
happy. we consider the algorithmic aspects of the following Maximum Happy Edges
(k-MHE) problem: given a partially k-colored graph G, find an extended full k-coloring
of G such that the number of happy edges are maximized. When we want to maximize
the number of happy vertices, the problem is known as Maximum Happy Vertices
(k-MHV).
We show that both k-MHE and k-MHV admit polynomial-time algorithms for trees.
We show that k-MHE admits a kernel of size k + `, where ` is the natural parameter,
the number of happy edges. We show the hardness of k-MHE and k-MHV for some
special graphs such as split graphs and bipartite graphs. We show that both k-MHE
and k-MHV are tractable for graphs with bounded tree-width and graphs with bounded
neighborhood diversity.
vii
In the last part of the thesis we present an algorithm for the Replacement Paths
Problem which is defined as follows: Let G (|V (G)| = n and |E(G)| = m) be an undirected
graph with positive edge weights. Let PG(s, t) be a shortest s − t path in G. Let l be the
number of edges in PG(s, t). The Edge Replacement Path problem is to compute a
shortest s − t path in G\{e}, for every edge e in PG(s, t). The Node Replacement
Path problem is to compute a shortest s−t path in G\{v}, for every vertex v in PG(s, t).
We present an O(TSP T (G) + m + l
2
) time and O(m + l
2
) space algorithm for both
the problems, where TSP T (G) is the asymptotic time to compute a single source shortest
path tree in G. The proposed algorithm is simple and easy to implement