The Tutte polynomial, also called the dichromate or the Tutte–Whitney polynomial, is a polynomial in two variables which plays an important role in graph theory, a branch of mathematics and theoretical computer science. It is defined for every undirected graph and contains information about how the graph is connected.
The importance of the Tutte polynomial T_{G} comes from the information it contains about G. Though originally studied in algebraic graph theory as a generalisation of counting problems related to graph coloring and nowherezero flow, it contains several famous other specialisations from other sciences such as the Jones polynomial from knot theory and the partition functions of the Potts model from statistical physics. It is also the source of several central computational problems in theoretical computer science.
The Tutte polynomial has several equivalent definitions. It is equivalent to Whitney’s rank polynomial, Tutte’s own dichromatic polynomial and Fortuin–Kasteleyn’s random cluster model under simple transformations. It is essentially a generating function for the number of edge sets of a given size and connected components, with immediate generalisations to matroids. It is also the most general graph invariant that can be defined by a deletion–contraction recurrence. Several textbooks about graph theory and matroid theory devote entire chapters to it.^{[1]}
Contents 
For an undirected graph G = (V,E) one may define the Tutte polynomial as
Here, c(A) denotes the number of connected components of the graph (V,A). In this definition it is clear that T_{G} is welldefined and a polynomial in x and y. The same definition can be given using slightly different notation by letting r(A) =  V  − c(A) denote the rank of the graph (V,A). Then the Whitney rank generating function is defined as
the two functions are equivalent under a simple change of variables: T_{G}(x,y) = R_{G}(x − 1,y − 1). Tutte’s dichromatic polynomial Q_{G} is the result of another simple transformation:
Tutte’s original definition of T_{G} is equivalent but less easily stated. For connected G we set
T_{G}(x,y) =  ∑  t_{ij}x^{i}y^{j}, 
i,j 
where t_{ij} denotes the number of spanning trees of “internal activity i and external activity j.”
A third definition uses a deletion–contraction recurrence. The edge contraction G / uv of graph G is the graph obtained by merging the vertices u and v and removing the edge uv. We write G − uv for the graph where the edge uv is merely removed. Then the Tutte polynomial is defined by the recurrence relation
with base case
Especially, T_{G} = 1 if G contains no edges.
The random cluster model from statistical mechanics provides yet another equivalent definition. The polynomial
is equivalent to T_{G} under the transformation
The Tutte polynomial factors into connected components: If G is the union of disjoint graphs H and H' then
If G is planar and G ^{*} denotes its dual graph then
Especially, the chromatic polynomial of a planar graph is the flow polynomial of its dual.
Isomorphic graphs have the same Tutte polynomial, but the opposite is not true. For example, the Tutte polynomial of every tree on m edges is x^{m}.
Tutte polynomials are often given in tabular form by listing the coefficients t_{ij} of x^{i}y^{j} in row i and column j. For example, the Tutte polynomial of the Petersen graph,
is given by the following table.
0  36  84  75  35  9  1 
36  168  171  65  10  
120  240  105  15  
180  170  30  
170  70  
114  12  
56  
21  
6  
1 
W. T. Tutte’s interest in the deletion–contraction formula started in his undergraduate days at Trinity College, Cambridge, originally motivated by perfect rectangles and spanning trees. He often applied the formula in his research and “wondered if there were other interesting functions of graphs, invariant under isomorphism, with similar recursion formulae.”^{[2]} R. M. Foster had already observed that the chromatic polynomial is one such function, and Tutte began to discover more. His original terminology for graph invariants that satisfy the delection–contraction recursion was Wfunction (and Vfunction if multiplicative over component). Tutte writes, “Playing with my Wfunctions I obtained a twovariable polynomial from which either the chromatic polynomial or the ﬂowpolynomial could be obtained by setting one of the variables equal to zero, and adjusting signs.”^{[2]} Tutte called this function the dichromate, as he saw it as a generalization of the chromatic polynomial to two variables, but it is usually referred to as the Tutte polynomial. In Tutte’s words, “This may be unfair to Hassler Whitney who knew and used analogous coefﬁcients without bothering to afﬁx them to two variables.” There is “notable confusion” ^{[3]} about the terms dichromate and dichromatic polynomial, introduced by Tutte in different papers and differ slightly. The generalisation of the Tutte polynomial to matroids was first published by Crapo, though it appears already in Tutte’s thesis.^{[4]}
Independently of the work in algebraic graph theory, Potts began studying the partition function of certain models in statistical mechanics in 1952. The work of Fortuin and Kasteleyn on the random cluster model, a generalisation of Pott’s model, provided a unifying expression that showed the relation to the Tutte polynomial.^{[5]}
At various points and lines of the (x,y)plane, the Tutte polynomial evaluates to quantities that have been studied in their own right in diverse fields of mathematics and physics. Part of the appeal of the Tutte polynomial comes from the unifying framework it provides for analysing these quantities.
At y = 0, the Tutte polynomial specialises to the chromatic polynomial,
where k(G) denote the number of connected components of G
For integer λ the value of chromatic polynomial P(G;λ) equals the number of vertex colourings of G using a set of λ colours. It is clear that P(G;λ) does not depend on the set of colours. What is less clear is that it is the evaluation at λ of a polynomial with integer coefficients. To see this, we observe:
The three conditions above enable us to calculate P(G;λ), by applying a sequence of edge deletions and contractions; but they give no guarantee that a different sequence of deletions and contractions will lead to the same value. The guarantee comes from the fact that P(G;λ) counts something, independently of the recurrence.
Especially, at (2,0), the Tutte polynomial counts the number of acyclic orientations.
Along the hyperbola xy = 1 the Tutte polynomial specialises to the Jones polynomial of an alternating knot if G is planar.
Along the hyperbola defined by (x − 1)(y − 1) = 2, the Tutte polynomial specialises to the partition function of the Ising model studied in statistical physics. More generally, along the hyperbolae H_{q} defined by (x − 1)(y − 1) = q for any positive integer q, the Tutte polynomial specialises to the partition function of the qstate Potts model. Various physical quantities analysed in the framework of the Potts model translate to specific parts of the H_{q}.
Potts model  Tutte polynomial 

Ferromagnetism  Positive branch of H_{q} 
Antiferromagnetism  Negative branch of H_{q} with y > 0 
High temperature  Asymptote of H_{q} to y = 1 
Low temperature ferromagnetic  Positive branch of H_{q} asymptotic to x = 1 
Zero temperature antiferromagnetic  Graph qcolouring, i.e., x = 1 − q,y = 0 
At x = 0, the Tutte polynomial specialises to the flow polynomial studied in combinatorics. For a connected and undirected graph G and integer k, a nowherezero kflow is an assignment of “flow” values to the edges of an arbitrary orientation of G such that the total flow entering and leaving each vertex is congruent modulo k. The flow polynomial C_{G}(k) denotes the number of nowherezero kflows. This value is intimately connected with the chromatic polynomial, in fact, if G is a planar graph, the chromatic polynomial of G is equivalent to the flow polynomial of its dual graph G ^{*} in the sense that
The connection to the Tutte polynomial is given by
At x = 1, the Tutte polynomial specialises to the allterminal reliability polynomial studied in network theory. For a connected graph G remove every edge with probability p; this models a network subject to random edge failures. Then the reliability polynomial is a function R(G,p), a polynomial in p, that gives the probability that every pair of vertices in G remains connected after the edges fail. The connection to the Tutte polynomial is given by
Tutte also defined a closer 2variable generalization of the chromatic polynomial, the dichromatic polynomial of a graph. This is
where c(A) is the number of connected components of the spanning subgraph (V,A). This is related to the coranknullity polynomial by
where c is the number of components of G. The dichromatic polynomial does not generalize to matroids because c is not a matroid property: different graphs with the same matroid can have different numbers of components.
The deletion–contraction recurrence for the Tutte polynomial,
immediately yields a recursive algorithm for computing it: choose any such edge e and repeatedly apply the formula until all edges are either loops or bridges; the resulting base cases at the bottom of the evaluation are easy to compute.
Within a polynomial factor, the running time t of this algorithm can be expressed in terms of the number of vertices n and the number of edges m of the graph,
a recurrence relation that scales as the Fibonacci numbers with solution t(n + m) = ((1 + √5) / 2)^{n + m} = O(1.6180^{n + m}) ^{[8]}. The analysis can be improved to within a polynomial factor of the number τ(G) of spanning trees of the input graph ^{[9]}. For sparse graphs with m = O(n) this running time is O(exp(n)). For regular graphs of degree k, the number of spanning trees can be bounded by
so the deletion–contraction algorithm runs within a polynomial factor of this bound. For example, for k = 5, the base of the exponent ν_{5} is around 4.4066 ^{[10]}
In practice, branch and bound strategies and graph isomorphism rejection are employed to avoid some recursive calls. This approach works well for graphs that are quite sparse and exhibit many symmetries; the performance of the algorithm depends on the heuristic used to pick the edge e ^{[11]}.
In some restricted instances, the Tutte polynomial can be computed in polynomial time, ultimately because Gaussian elimination efficiently computes the matrix operations determinant and Pfaffian. These algorithms are themselves important results from algebraic graph theory and statistical mechanics.
T_{G}(1,1) equals the number τ(G) of spanning trees of a connected graph. This is computable in polynomial time as the determinant of a maximal principal submatrix of the Laplacian matrix of G, an early result in algebraic graph theory known as Kirchhoff’s Matrix–Tree theorem. Likewise, the dimension of the bicycle space at T_{G}( − 1, − 1) can be computed in polynomial time by Gaussian elimination.
For planar graphs, the partition function of the Ising model, i.e., the Tutte polynomial at the hyperbola (x − 1)(y − 1) = 2, can be expressed as a Pfaffian. This idea was developed by Kasteleyn and Michael Fisher to compute for the number of dimer covers of a planar lattice model.
Using a Markov chain Monte Carlo method, the Tutte polynomial can be arbitrarily well approximated along the positive branch of H_{2}, equivalently, the partition function of the antiferromagnetic Ising model. This exploits the close connection between the Ising model and the problem of counting matchings in a graph. The idea behind this celebrated result of Jerrum and Sinclair^{[12]} is to set up a Markov chain whose states are the matchings of the input graph. The transitions are defined by choosing edges at random and modifying the matching accordingly. The resulting Markov chain is rapidly mixing and leads to “sufficiently random” matchings, which can be used to recover the partition function using random sampling. The resulting algorithm is a fully polynomialtime randomized approximation scheme (fpras).
Several computational problems are associated with the Tutte polynomial. The most straightforward one is
Especially, the output allows evaluating T_{G} at ( − 2,0), equivalent to counting the number of 3colourings of G This latter question is #Pcomplete even restricted to the family of planar graphs, so the problem of computing the coefficients of the Tutte polynomial for a given graph is #Phard even for planar graphs.
Much more attention has been given to the family of problems called Tutte(x,y) defined for every rational pair (x,y):
The hardness of these problems varies with the coordinates (x,y).
If both x and y are nonnegative integers, the problem Tutte(x,y) belongs to #P. For general integer pairs, the Tutte polynomial contains negative terms, which places the problem in the complexity class GapP, the closure under subtraction of #P. To accommodate rational coordinates (x,y), one can define a rational analogue of #P.^{[13]}
The #Phardness of exactly solving Tutte(x,y) is completely understood. The problem is polynomialtime computable if (x,y) is one of the points (1, 1), (1,1), (0,1), (1,0), or satisfies (x − 1)(y − 1) = 1. Otherwise it is #Phard^{[14]}. If the problem is restricted to the class of planar graphs, the points on the hyperbola defined by (x − 1)(y − 1) = 2 become polynomialtime computable, but all other points remain #Phard. This result contains several notable special cases. For example, the problem of computing the partition function of the Ising model is #Phard in general, even though celebrated algorithms of Onsager and Fisher solve it for planar lattices. Also, the Jones polynomial is #Phard to compute. Finally, computing the number of fourcolourings of a planar graph is hard (in fact, #Pcomplete), even though the decision problem is trivial by the four color theorem. In contrast, it is easy to see that counting the number of threecolourings for planar graphs is hard (in fact, NPhard), because the decision problem is known to be NPcomplete.
The question which points admit a good approximation algorithm has been very well studied. Apart from the points already in P, the only approximation algorithm known for Tutte(x,y) is Jerrum and Sinclair’s FPRAS, which works for points on the “Ising” hyperbola (x − 1)(y − 1) = 2 for y>0. If the input graphs are restricted to dense instances, with degree Ω(n), there is an FPRAS if x ≥ 1, y ≥ 1.^{[15]}
Even though the situation is not as well understood as for exact computation, large areas of the plane are known to be hard to approximate.^{[13]}
