A Simple Polynomial Time Algorithm for Max Cut on Laminar Geometric Intersection Graphs

In a geometric intersection graph, given a collection of n geometric objects as input, each object corresponds to a vertex and there is an edge between two vertices if and only if the corresponding objects intersect. In this work, we present a somewhat surprising result: a polynomial time algorithm for max cut on laminar geometric intersection graphs. In a laminar geometric intersection graph, if two objects intersect, then one of them will completely lie inside the other. To the best of our knowledge, for max cut this is the first class of (non-trivial) geometric intersection graphs with an exact solution in polynomial time. Our algorithm uses a simple greedy strategy. However, proving its correctness requires non-trivial ideas. Next, we design almost-linear time algorithms (in terms of n) for laminar axis-aligned boxes by combining the properties of laminar objects with vertical ray shooting data structures. Note that the edge-set of the graph is not explicitly given as input; only the n geometric objects are given as input

On the Maximum Cardinality Cut Problem in Proper Interval Graphs and Related Graph Classes

Although it has been claimed in two different papers that the maximum cardinality cut problem is polynomial-time solvable for proper interval graphs, both of them turned out to be erroneous. In this paper, we give FPT algorithms for the maximum cardinality cut problem in classes of graphs containing proper interval graphs and mixed unit interval graphs when parameterized by some new parameters that we introduce. These new parameters are related to a generalization of the so-called bubble representations of proper interval graphs and mixed unit interval graphs and to clique-width decompositions

U-Bubble Model for Mixed Unit Interval Graphs and Its Applications: The MaxCut Problem Revisited

Interval graphs, intersection graphs of segments on a real line (intervals), play a key role in the study of algorithms and special structural properties. Unit interval graphs, their proper subclass, where each interval has a unit length, has also been extensively studied. We study mixed unit interval graphs - a generalization of unit interval graphs where each interval has still a unit length, but intervals of more than one type (open, closed, semi-closed) are allowed. This small modification captures a much richer class of graphs. In particular, mixed unit interval graphs are not claw-free, compared to unit interval graphs. Heggernes, Meister, and Papadopoulos defined a representation of unit interval graphs called the bubble model which turned out to be useful in algorithm design. We extend this model to the class of mixed unit interval graphs and demonstrate the advantages of this generalized model by providing a subexponential-time algorithm for solving the MaxCut problem on mixed unit interval graphs. In addition, we derive a polynomial-time algorithm for certain subclasses of mixed unit interval graphs. We point out a substantial mistake in the proof of the polynomiality of the MaxCut problem on unit interval graphs by Boyaci, Ekim, and Shalom (2017). Hence, the time complexity of this problem on unit interval graphs remains open. We further provide a better algorithmic upper-bound on the clique-width of mixed unit interval graphs

Maximum Cut on Interval Graphs of Interval Count Four Is NP-Complete

The computational complexity of the MaxCut problem restricted to interval graphs has been open since the 80's, being one of the problems proposed by Johnson on his Ongoing Guide to NP-completeness, and has been settled as NP-complete only recently by Adhikary, Bose, Mukherjee and Roy. On the other hand, many flawed proofs of polynomiality for MaxCut on the more restrictive class of unit/proper interval graphs (or graphs with interval count 1) have been presented along the years, and the classification of the problem is still unknown. In this paper, we present the first NP-completeness proof for MaxCut when restricted to interval graphs with bounded interval count, namely graphs with interval count 4

Exploiting structure to cope with NP-hard graph problems: Polynomial and exponential time exact algorithms

An ideal algorithm for solving a particular problem always finds an optimal solution, finds such a solution for every possible instance, and finds it in polynomial time. When dealing with NP-hard problems, algorithms can only be expected to possess at most two out of these three desirable properties. All algorithms presented in this thesis are exact algorithms, which means that they always find an optimal solution. Demanding the solution to be optimal means that other concessions have to be made when designing an exact algorithm for an NP-hard problem: we either have to impose restrictions on the instances of the problem in order to achieve a polynomial time complexity, or we have to abandon the requirement that the worst-case running time has to be polynomial. In some cases, when the problem under consideration remains NP-hard on restricted input, we are even forced to do both. Most of the problems studied in this thesis deal with partitioning the vertex set of a given graph. In the other problems the task is to find certain types of paths and cycles in graphs. The problems all have in common that they are NP-hard on general graphs. We present several polynomial time algorithms for solving restrictions of these problems to specific graph classes, in particular graphs without long induced paths, chordal graphs and claw-free graphs. For problems that remain NP-hard even on restricted input we present exact exponential time algorithms. In the design of each of our algorithms, structural graph properties have been heavily exploited. Apart from using existing structural results, we prove new structural properties of certain types of graphs in order to obtain our algorithmic results

Novel procedures for graph edge-colouring

Orientador: Dr. Renato CarmoCoorientador: Dr. André Luiz Pires GuedesTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 05/12/2018Inclui referências e índiceÁrea de concentração: Ciência da ComputaçãoResumo: O índice cromático de um grafo G é o menor número de cores necessário para colorir as arestas de G de modo que não haja duas arestas adjacentes recebendo a mesma cor. Pelo célebre Teorema de Vizing, o índice cromático de qualquer grafo simples G ou é seu grau máximo , ou é ? + 1, em cujo caso G é dito Classe 1 ou Classe 2, respectivamente. Computar uma coloração de arestas ótima de um grafo ou simplesmente determinar seu índice cromático são problemas NP-difíceis importantes que aparecem em aplicações notáveis, como redes de sensores, redes ópticas, controle de produção, e jogos. Neste trabalho, nós apresentamos novos procedimentos de tempo polinomial para colorir otimamente as arestas de grafos pertences a alguns conjuntos grandes. Por exemplo, seja X a classe dos grafos cujos maiorais (vértices de grau ?) possuem soma local de graus no máximo ?2 ?? (entendemos por 'soma local de graus' de um vértice x a soma dos graus dos vizinhos de x). Nós mostramos que quase todo grafo está em X e, estendendo o procedimento de recoloração que Vizing usou na prova para seu teorema, mostramos que todo grafo em X é Classe 1. Nós também conseguimos resultados em outras classes de grafos, como os grafos-junção, os grafos arco-circulares, e os prismas complementares. Como um exemplo, nós mostramos que um prisma complementar só pode ser Classe 2 se for um grafo regular distinto do K2. No que diz respeito aos grafos-junção, nós mostramos que se G1 e G2 são grafos disjuntos tais que |V(G1)| _ |V(G2)| e ?(G1) _ ?(G2), e se os maiorais de G1 induzem um grafo acíclico, então o grafo-junção G1 ?G2 é Classe 1. Além desses resultados em coloração de arestas, apresentamos resultados parciais em coloração total de grafos-junção, de grafos arco-circulares, e de grafos cobipartidos, bem como discutimos um procedimento de recoloração para coloração total. Palavras-chave: Coloração de grafos e hipergrafos (MSC 05C15). Algoritmos de grafos (MSC 05C85). Teoria dos grafos em relação à Ciência da Computação (MSC 68R10). Graus de vértices (MSC 05C07). Operações de grafos (MSC 05C76).Abstract: The chromatic index of a graph G is the minimum number of colours needed to colour the edges of G in a manner that no two adjacent edges receive the same colour. By the celebrated Vizing's Theorem, the chromatic index of any simple graph G is either its maximum degree ? or it is ? + 1, in which case G is said to be Class 1 or Class 2, respectively. Computing an optimal edge-colouring of a graph or simply determining its chromatic index are important NP-hard problems which appear in noteworthy applications, like sensor networks, optical networks, production control, and games. In this work we present novel polynomial-time procedures for optimally edge-colouring graphs belonging to some large sets of graphs. For example, let X be the class of the graphs whose majors (vertices of degree ?) have local degree sum at most ?2 ? ? (by 'local degree sum' of a vertex x we mean the sum of the degrees of the neighbours of x). We show that almost every graph is in X and, by extending the recolouring procedure used by Vizing's in the proof for his theorem, we show that every graph in X is Class 1. We further achieve results in other graph classes, such as join graphs, circular-arc graphs, and complementary prisms. For instance, we show that a complementary prism can be Class 2 only if it is a regular graph distinct from the K2. Concerning join graphs, we show that if G1 and G2 are disjoint graphs such that |V(G1)| _ |V(G2)| and ?(G1) _ ?(G2), and if the majors of G1 induce an acyclic graph, then the join graph G1 ?G2 is Class 1. Besides these results on edge-colouring, we present partial results on total colouring join graphs, cobipartite graphs, and circular-arc graphs, as well as a discussion on a recolouring procedure for total colouring. Keywords: Colouring of graphs and hypergraphs (MSC 05C15). Graph algorithms (MSC 05C85). Graph theory in relation to Computer Science (MSC 68R10). Vertex degrees (MSC 05C07). Graph operations (MSC 05C76)

Graphs with at most two moplexes

A moplex is a natural graph structure that arises when lifting Dirac's classical theorem from chordal graphs to general graphs. However, while every non-complete graph has at least two moplexes, little is known about structural properties of graphs with a bounded number of moplexes. The study of these graphs is motivated by the parallel between moplexes in general graphs and simplicial modules in chordal graphs: Unlike in the moplex setting, properties of chordal graphs with a bounded number of simplicial modules are well understood. For instance, chordal graphs having at most two simplicial modules are interval. In this work we initiate an investigation of $k$-moplex graphs, which are defined as graphs containing at most $k$ moplexes. Of particular interest is the smallest nontrivial case $k=2$, which forms a counterpart to the class of interval graphs. As our main structural result, we show that the class of connected $2$-moplex graphs is sandwiched between the classes of proper interval graphs and cocomparability graphs; moreover, both inclusions are tight for hereditary classes. From a complexity theoretic viewpoint, this leads to the natural question of whether the presence of at most two moplexes guarantees a sufficient amount of structure to efficiently solve problems that are known to be intractable on cocomparability graphs, but not on proper interval graphs. We develop new reductions that answer this question negatively for two prominent problems fitting this profile, namely Graph Isomorphism and Max-Cut. On the other hand, we prove that every connected $2$-moplex graph contains a Hamiltonian path, generalising the same property of connected proper interval graphs. Furthermore, for graphs with a higher number of moplexes, we lift the previously known result that graphs without asteroidal triples have at most two moplexes to the more general setting of larger asteroidal sets

The k-fixed-endpoint path partition problem

The Hamiltonian path problem is to determine whether a graph has a Hamiltonian path. This problem is NP-complete in general. The path partition problem is to determine the minimum number of vertex-disjoint paths required to cover a graph. Since this problem is a generalization of the Hamiltonian path problem, it is also NP-complete in general. The k-fixed-endpoint path partition problem is to determine the minimum number of vertex-disjoint paths required to cover a graphG such that each vertex in a set T of k vertices is an endpoint of a path. Since this problem is a generalization of the Hamiltonian path problem and path partition problem, it is also NP-complete in general. For certain classes of graphs, there exist efficient algorithms for the k-fixed-endpoint path partition problem. We consider this problem restricted to trees, threshold graphs, block graphs, and unit interval graphs and show min-max theorems which characterize the k-fixed-endpoint pathpartition number

Generalizations of comparability graphs

2022 Summer.Includes bibliographical references.In rational decision-making models, transitivity of preferences is an important principle. In a transitive preference, one who prefers x to y and y to z must prefer x to z. Many preference relations, including total order, weak order, partial order, and semiorder, are transitive. As a preference which is transitive yet not all pairs of elements are comparable, partial orders have been studied extensively. In graph theory, a comparability graph is an undirected graph which connects all comparable elements in a partial order. A transitive orientation is an assignment of direction to every edge so that the resulting directed graph is transitive. A graph is transitive if there is such an assignment. Comparability graphs are a class of graphs where clique, coloring, and many other optimization problems are solved by polynomial algorithms. It also has close connections with other classes of graphs, such as interval graphs, permutation graphs, and perfect graphs. In this dissertation, we define new measures for transitivity to generalize comparability graphs. We introduce the concept of double threshold digraphs together with a parameter λ which we define as our degree of transitivity. We also define another measure of transitivity, β, as the longest directed path such that there is no edge from the first vertex to the last vertex. We present approximation algorithms and parameterized algorithms for optimization problems and demonstrate that they are efficient for "almost-transitive" preferences