Graph Colouring with Input Restrictions

In this thesis, we research the computational complexity of the graph colouring problem and its variants including precolouring extension and list colouring for graph classes that can be characterised by forbidding one or more induced subgraphs. We investigate the structural properties of such graph classes and prove a number of new properties. We then consider to what extent these properties can be used for efficiently solving the three types of colouring problems on these graph classes. In some cases we obtain polynomial-time algorithms, whereas other cases turn out to be NP-complete. We determine the computational complexity of k-COLOURING, k-PRECOLOURING EXTENSION and LIST k-COLOURING on $P_k$-free graphs. In particular, we prove that k-COLOURING on $P_8$-free graphs is NP-complete, 4-PRECOLOURING EXTENSION $P_7$-free graphs is NP-complete, and LIST 4-COLOURING on $P_6$-free graphs is NP-complete. In addition, we show the existence of an integer r such that k-COLOURING is NP-complete for $P_r$-free graphs with girth 4. In contrast, we determine for any fixed girth $g\geq 4$ a lower bound $r(g)$ such that every $P_{r(g)}$-free graph with girth at least $g$ is 3-colourable. We also prove that 3-LIST COLOURING is NP-complete for complete graphs minus a matching. We present a polynomial-time algorithm for solving 4-PRECOLOURING EXTENSION on $(P_2+P_3)$-free graphs, a polynomial-time algorithm for solving LIST 3-Colouring on $(P_2+P_4)$-free graphs, and a polynomial-time algorithm for solving LIST 3-COLOURING on $sP_3$-free graphs. We prove that LIST k-COLOURING for $(K_{s,t},P_r)$-free graphs is also polynomial-time solvable. We obtain several new dichotomies by combining the above results with some known results

Structural solutions to maximum independent set and related problems

In this thesis, we study some fundamental problems in algorithmic graph theory. Most natural problems in this area are hard from a computational point of view. However, many applications demand that we do solve such problems, even if they are intractable. There are a number of methods in which we can try to do this: 1) We may use an approximation algorithm if we do not necessarily require the best possible solution to a problem. 2) Heuristics can be applied and work well enough to be useful for many applications. 3) We can construct randomised algorithms for which the probability of failure is very small. 4) We may parameterize the problem in some way which limits its complexity. In other cases, we may also have some information about the structure of the instances of the problem we are trying to solve. If we are lucky, we may and that we can exploit this extra structure to find efficient ways to solve our problem. The question which arises is - How far must we restrict the structure of our graph to be able to solve our problem efficiently? In this thesis we study a number of problems, such as Maximum Indepen- dent Set, Maximum Induced Matching, Stable-II, Efficient Edge Domina- tion, Vertex Colouring and Dynamic Edge-Choosability. We try to solve problems on various hereditary classes of graphs and analyse the complexity of the resulting problem, both from a classical and parameterized point of view