The chromatic polynomial is a polynomial studied in algebraic graph theory, a branch of mathematics. It counts the number of graph colorings as a function of the number of colors and was originally defined by George David Birkhoff to attack the four color problem. It was generalised to the Tutte polynomial by H. Whitney and W. T. Tutte, linking it to the Potts model of statistical physics.
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George David Birkhoff introduced the chromatic polynomial in 1912, defining it only for planar graphs, in an attempt to prove the four color theorem. If P(G,k) denotes the number of proper colorings of G with k colors then one could establish the four color theorem by showing P(G,4) > 0 for all planar graphs G. In this way he hoped to apply the powerful tools of analysis and algebra for studying the roots of polynomials to the combinatorial coloring problem.
Hassler Whitney generalised Birkhoff’s polynomial from the planar case to general graphs in 1932. In 1968 Read asked which polynomials are the chromatic polynomials of some graph, a question that remains open, and introduced the concept of chromatically equivalent graphs. Today, chromatic polynomials are one of the central objects of algebraic graph theory^{[1]}
The chromatic polynomial of a graph counts the number of its proper vertex colorings. It is commonly denoted P_{G}(k), χ_{G}(k), or π_{G}(k), and sometimes in the form P(G;k), where it is understood that for fixed G the function is a polynomial in k, the number of colors.
For example, the path graph P_{3} on 3 vertices cannot be colored at all with 0 or 1 colors. With 2 colors, it can be colored in 2 ways. With 3 colors, it can be colored in 12 ways.
Available colors k  0  1  2  3 
Number of colorings P_{G}(k)  0  0  2  12 
The chromatic polynomial is defined as the unique interpolating polynomial of degree n through the points (k,P_{G}(k)) for . For the example graph, P_{G}(k) = k(k − 1)^{2}, and indeed P_{G}(3) = 12.
The chromatic polynomial includes at least as much information about the colorability of G as does the chromatic number. Indeed, the chromatic number is the smallest positive integer that is not a root of the chromatic polynomial,
Triangle K_{3}  t(t − 1)(t − 2) 
Path graph P_{n}  t(t − 1)^{n − 1} 
Complete graph K_{n}  
Tree with n vertices  t(t − 1)^{n − 1} 
Cycle C_{n}  (t − 1)^{n} + ( − 1)^{n}(t − 1) 
Petersen graph  t(t − 1)(t − 2)(t^{7} − 12t^{6} + 67t^{5} − 230t^{4} + 529t^{3} − 814t^{2} + 775t − 352) 
For fixed G on n vertices, the chromatic polynomial P(G,t) is in fact a polynomial; it has degree n. Nonisomorphic graphs may have the same chromatic polynomial. By definition, evaluating the chromatic polynomial in P(G,k) yields the number of kcolorings of G for k = 1,2,…n. Perhaps surprisingly, ( − 1) ^{ V(G) } P(G, − 1) yields the number of acyclic orientations of G^{[2]}. Furthermore, the derivative evaluated at unity, P'(G,1) equals the chromatic invariant θ(G) up to sign.
If G has n vertices, m edges, and k components G_{1},G_{2},…,G_{k}, then
A graph G with n vertices is a tree if and only if P(G,t) = t(t − 1)^{n − 1}.
Two graphs are said to be chromatically equivalent if they have the same chromatic polynomial. Isomorphic graphs have the same chromatic polynomial, but nonisomorphic graphs can be chromatically equivalent. For example, all trees on n vertices have the same chromatic polynomial; in particular, (x − 1)^{3}x is the chromatic polynomial of both the claw graph and the path graph on 4 vertices.
A graph is chromatically unique if it is determined by its chromatic polynomial, up to isomorphism. In other words, G is chromatically unique if P_{G} = P_{H} implies that G and H are isomorphic. All Cycle graphs are chromatically unique.^{[3]}
A root (or zero) of a chromatic polynomial, called a “chromatic root”, is a value x where P_{G}(x) = 0. Chromatic roots have been very well studied, in fact, Birkhoff’s original motivation for defining the chromatic polynomial was to show that for planar graphs, P_{G}(x) > 0 for x ≥ 4. This would have established the four color theorem.
No graph can be 0colored, so 0 is always a chromatic root. Only edgeless graphs can be 1colored, so 1 is a chromatic root for every graph with at least an edge. On the other hand, except for these two points, no graph can have a chromatic root at a real number smaller than or equal to 32/28. A results of Tutte connects the golden ratio φ with the study of chromatic roots, showing that chromatic roots exist very close to φ^{2}: If G_{n} is a planar triangulation of a sphere then P(G_{n},φ) ≤ φ^{5 − n}. While the real line thus has large parts that contain no chromatic roots for any graph, the every point in the complex plane is arbitrarily close to a chromatic root in the sense that there exists an infinite family of graphs whose chromatic roots are dense in the complex plane.^{[4]}
Chromatic polynomial  
Input  Graph G with n vertices. 
Output  Coefficients of P_{G} 
Running time  O(2^{n}n^{ r}) for some constant r 
Complexity  #Phard 
Reduction from  #3SAT 
#kcolorings  
Input  Graph G with n vertices. 
Output  P_{G}(k) 
Running time  In P for x = 0,1,2. O(1.6262^{n}) for x = 3 O(1.9464^{n}) for x = 4. Otherwise O(2^{n}n^{ r}) for some constant r 
Complexity  #Phard unless x = 0,1,2 
Approximability  No FPRAS for x > 2 
Computational problems associated with the chromatic polynomial include
The first problem is more general because if we knew the coefficients of P_{G} we could evaluate it at any point in polynomial time because the degree is n. The difficulty of the second type of problem depends strongly on the value of k and has been intensively studied in computational complexity.
For some basic graph classes, closed formulas for the chromatic polynomial are known. For instance this is true for trees and cliques, as listed in the table above.
Polynomial time algorithms are known for computing the chromatic polynomial for wider classes of graphs, including chordal graphs^{[5]} and graphs of bounded cliquewidth.^{[6]} The latter class includes cographs and graphs of bounded treewidth, such as outerplanar graphs.
A recursive way of computing the chromatic polynomial is based on edge contraction: for a pair of vertices u and v the graph G / uv is obtained by merging the two vertices and removing any edges between them. Then the chromatic polynomial satisfies the recurrence relation
where u and v are adjacent vertices and G − uv is the graph with the edge uv removed. Equivalently,
if u and v are not adjacent and G + uv is the graph with the edge uv added. In the first form, the recurrence terminates in a collection of empty graphs. In the second form, it terminates in a collection of complete graphs. These recurrences are also called the Fundamental Reduction Theorem^{[7]} Tutte’s curiosity about which other graph properties satisfied such recurrences led him to discover a bivariate generalization of the chromatic polynomial, the Tutte polynomial.
The expressions give rise to a recursive procedure, called the deletion–contraction algorithm, which forms the basis of many algorithms for graph coloring. The ChromaticPolynomial function in the computer algebra system Mathematica uses the second recurrence if the graph is dense, and the first recurrence if the graph is sparse^{[8]} The worst case running time of either formula satisfies the same recurrence relation as the Fibonacci numbers, so in the worst case, the algorithm runs in time within a polynomial factor of ^{[9]}. The analysis can be improved to within a polynomial factor of the number t(G) of spanning trees of the input graph ^{[10]}. In practice, branch and bound strategies and graph isomorphism rejection are employed to avoid some recursive calls, the running time depends on the heuristic used to pick the vertex pair.
The problem of computing the number of 3colorings of a given graph is a canonical example of a #Pcomplete problem, so the problem of computing the coefficients of the chromatic polynomial is #Phard. Similarly, evaluating P_{G}(3) for given G is #Pcomplete. On the other hand, for k = 0,1,2 it is easy to compute P_{G}(k), so the corresponding problems are polynomialtime computable. For integers k > 3 the problem is #Phard, which is established similar to the case k = 3. In fact, it in known that P_{G}(x) is #Phard for all x (including negative integers and nonintegers) except for the three “easy points”^{[11]} Thus, from the perspective of #Phardness, the complexity of computing the chromatic polynomial is completely understood.
In the expansion , the coefficient a_{n} is always equal to 1, and several other properties of the coefficients are known. This raises the question if some of the coefficients are easy to compute. However the computational problem of computing the rth coefficient of P_{G} for fixed r for given graph G is hard for #P.^{[12]}
No approximation algorithms for computing P_{G}(x) are known for any x except for the three easy points. At the integer points , the corresponding decision problem of deciding if a given graph can be kcolored is NPhard. Such problems cannot be approximated to any multiplicative factor by a boundederror probabilistic algorithm unless NP = RP, because any multiplicative approximation would distinguish the values 0 and 1, effectively solving the decision version in boundederror probabilistic polynomial time. In particular, under the same assumption, this rules out the possibility of a fully polynomial time randomised approximation scheme (FPRAS). For other points, more complicated arguments are needed, and the question is the focus of active research. As of 2008, it is known that there is no FPRAS for computing P_{G}(x) for any x > 2 unless NP = RP holds.^{[13]}
