By R. M. R. Lewis

ISBN-10: 3319257307

ISBN-13: 9783319257303

This ebook treats graph colouring as an algorithmic challenge, with a powerful emphasis on useful functions. the writer describes and analyses a number of the best-known algorithms for colouring arbitrary graphs, targeting even if those heuristics delivers optimum strategies sometimes; how they practice on graphs the place the chromatic quantity is unknown; and whether or not they can produce higher ideas than different algorithms for particular types of graphs, and why.

The introductory chapters clarify graph colouring, and boundaries and positive algorithms. the writer then indicates how complex, glossy suggestions will be utilized to vintage real-world operational examine difficulties reminiscent of seating plans, activities scheduling, and college timetabling. He comprises many examples, feedback for extra examining, and ancient notes, and the ebook is supplemented by way of an internet site with an internet suite of downloadable code.

The booklet can be of worth to researchers, graduate scholars, and practitioners within the parts of operations examine, theoretical laptop technology, optimization, and computational intelligence. The reader must have trouble-free wisdom of units, matrices, and enumerative combinatorics.

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Additional resources for A Guide to Graph Colouring: Algorithms and Applications

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Chapter 2 Bounds and Constructive Algorithms Towards the end of Chap. 1 we saw a variety of different types of graphs that are relatively straightforward to colour optimally, including complete graphs, bipartite graphs, cycle and wheel graphs, and grid graphs. With regard to the chromatic number, we also saw that it is easy to determine when χ(G) = 1 (G is an empty graph), and when χ(G) = 2 (G is bipartite). But can we go further than this? In this chapter we review and analyse a number of fast constructive algorithms for the graph colouring problem.

A) v5 v6 (b) v2 v1 v3 v4 v5 v1 v3 v6 Fig. 1(a) shows a graph G where, for example, vertices v1 and v3 are adjacent, but v1 and v2 are nonadjacent. The neighbourhood of v1 is Γ (v1 ) = {v3 , v5 }, giving deg(v1 ) = 2. 467. 1(b) has been created via the operation G − {v2 , v4 }, and in this case both G and G are connected. Paths in G from, for example, v1 to v6 include (v1 , v3 , v4 , v5 , v6 ) (of length 4) and (v1 , v5 , v6 ) (of length 2). Since the latter path is also the shortest path between v1 to v6 , the distance between these vertices is also 2.

Given a set of intervals defined on the real line, an interval graph is defined as a graph in which adjacent vertices correspond to overlapping intervals. 9 Let I = {I1 , . . , In } be a set of intervals defined on the real line such that each interval Ii = {x ∈ R : ai ≤ x < bi }, where ai and bi define the start and end values of interval Ii . The interval graph of I is the graph G = (V, E) for which V = {v1 , . . , vn } and where E = {{vi , v j } : Ii ∩ I j = 0}. 3 where we sought to assign taxi journeys with known start and end times to a minimal number of vehicles.

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A Guide to Graph Colouring: Algorithms and Applications by R. M. R. Lewis

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