Monochromatic triangle

Last updated September 18, 2019

In graph theory and theoretical computer science, the monochromatic triangle problem is an algorithmic problem on graphs, in which the goal is to partition the edges of a given graph into two triangle-free subgraphs. It is NP-complete but fixed-parameter tractable on graphs of bounded treewidth.

Graph theory Area of discrete mathematics

In mathematics, graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices which are connected by edges. A distinction is made between undirected graphs, where edges link two vertices symmetrically, and directed graphs, where edges link two vertices asymmetrically; see Graph for more detailed definitions and for other variations in the types of graph that are commonly considered. Graphs are one of the prime objects of study in discrete mathematics.

Theoretical computer science subfield of computer science and of mathematics

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on more mathematical topics of computing and includes the theory of computation.

In the mathematical area of graph theory, a triangle-free graph is an undirected graph in which no three vertices form a triangle of edges. Triangle-free graphs may be equivalently defined as graphs with clique number ≤ 2, graphs with girth ≥ 4, graphs with no induced 3-cycle, or locally independent graphs.

Problem statement

The monochromatic triangle problem takes as input an n-node undirected graph G(V,E) with node set V and edge set E. The output is a Boolean value, true if the edge set E of G can be partitioned into two disjoint sets E1 and E2, such that both of the two subgraphs G1(V,E1) and G2(V,E2) are triangle-free graphs, and false otherwise. This decision problem is NP-complete.^[1]

In computability theory and computational complexity theory, a decision problem is a problem that can be posed as a yes-no question of the input values. An example of a decision problem is deciding whether a given natural number is prime. Another is the problem "given two numbers x and y, does x evenly divide y?". The answer is either 'yes' or 'no' depending upon the values of x and y. A method for solving a decision problem, given in the form of an algorithm, is called a decision procedure for that problem. A decision procedure for the decision problem "given two numbers x and y, does x evenly divide y?" would give the steps for determining whether x evenly divides y. One such algorithm is long division. If the remainder is zero the answer is 'yes', otherwise it is 'no'. A decision problem which can be solved by an algorithm is called decidable.

Generalization to multiple colors

The problem may be generalized to triangle-free edge coloring, finding an assignment of colors to the edges of a graph so that no triangle has all three edges given the same color. The monochromatic triangle problem is the special case of triangle-free edge-coloring when there are exactly two colors available. If there exists a two-color triangle-free edge coloring, then the edges of each color form the two sets E1 and E2 of the monochromatic triangle problem. Conversely, if the monochromatic triangle problem has a solution, we can use one color for E1 and a second color for E2 to obtain a triangle-free edge coloring.

Connection to Ramsey's theorem

By Ramsey's theorem, for any finite number k of colors, there exists a number n such that complete graphs of n or more vertices do not have triangle-free edge colorings with k colors. For k = 2, the corresponding value of n is 6. That is, the answer to the monochromatic triangle problem on the complete graph K₆ is no.

In combinatorial mathematics, Ramsey's theorem states that one will find monochromatic cliques in any edge labelling of a sufficiently large complete graph. To demonstrate the theorem for two colours, let r and s be any two positive integers. Ramsey's theorem states that there exists a least positive integer R(r, s) for which every blue-red edge colouring of the complete graph on R(r, s) vertices contains a blue clique on r vertices or a red clique on s vertices.

Parameterized complexity

It is straightforward to express the monochromatic triangle problem in the monadic second-order logic of graphs (MSO₂), by a logical formula that asserts the existence of a partition of the edges into two subsets such that there do not exist three mutually adjacent vertices whose edges all belong to the same side of the partition. It follows from Courcelle's theorem that the monochromatic triangle problem is fixed-parameter tractable on graphs of bounded treewidth. More precisely, there is an algorithm for solving the problem whose running time is the number of vertices of the input graph multiplied by a quickly-growing but computable function of the treewidth.^[2]

In mathematical logic, monadic second-order logic (MSO) is the fragment of second-order logic where the second-order quantification is limited to quantification over sets. It is particularly important in the logic of graphs, because of Courcelle's theorem, which provides algorithms for evaluating monadic second-order formulas over graphs of bounded treewidth.

In the mathematical fields of graph theory and finite model theory, the logic of graphs deals with formal specifications of graph properties using formulas of mathematical logic. There are several variations in the types of logical operation that can be used in these formulas. The first order logic of graphs concerns formulas in which the variables and predicates concern individual vertices and edges of a graph, while monadic second order graph logic allows quantification over sets of vertices or edges. Logics based on least fixed point operators allow more general predicates over tuples of vertices, but these predicates can only be constructed through fixed-point operators, restricting their power to an intermediate level between first order and monadic second order.

In the study of graph algorithms, Courcelle's theorem is the statement that every graph property definable in the monadic second-order logic of graphs can be decided in linear time on graphs of bounded treewidth. The result was first proved by Bruno Courcelle in 1990 and independently rediscovered by Borie, Parker & Tovey (1992). It is considered the archetype of algorithmic meta-theorems.

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In graph theory, a planar graph is a graph that can be embedded in the plane, i.e., it can be drawn on the plane in such a way that its edges intersect only at their endpoints. In other words, it can be drawn in such a way that no edges cross each other. Such a drawing is called a plane graph or planar embedding of the graph. A plane graph can be defined as a planar graph with a mapping from every node to a point on a plane, and from every edge to a plane curve on that plane, such that the extreme points of each curve are the points mapped from its end nodes, and all curves are disjoint except on their extreme points.

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In graph theory, the tree-depth of a connected undirected graph G is a numerical invariant of G, the minimum height of a Trémaux tree for a supergraph of G. This invariant and its close relatives have gone under many different names in the literature, including vertex ranking number, ordered chromatic number, and minimum elimination tree height; it is also closely related to the cycle rank of directed graphs and the star height of regular languages. Intuitively, where the treewidth graph width parameter measures how far a graph is from being a tree, this parameter measures how far a graph is from being a star.

References

↑ Garey, Michael R.; Johnson, David S. (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, ISBN 978-0-7167-1045-5 . A1.1: GT6, pg.191.
↑ Arnborg, Stefan; Lagergren, Jens; Seese, Detlef (1988), "Problems easy for tree-decomposable graphs (extended abstract)", Automata, languages and programming (Tampere, 1988), Lecture Notes in Computer Science, 317, Berlin: Springer, pp. 38–51, doi:10.1007/3-540-19488-6_105, MR 1023625 .

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[1] Garey, Michael R.; Johnson, David S. (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman, ISBN 978-0-7167-1045-5 . A1.1: GT6, pg.191.

[2] Arnborg, Stefan; Lagergren, Jens; Seese, Detlef (1988), "Problems easy for tree-decomposable graphs (extended abstract)", Automata, languages and programming (Tampere, 1988), Lecture Notes in Computer Science, 317, Berlin: Springer, pp. 38–51, doi:10.1007/3-540-19488-6_105, MR 1023625 .