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Dec 15, 2016

🖉 Combinatorial Nullstellensatz and List Coloring

More than six months late, but here are notes from the combinatorial nullsetllensatz talk I gave at the student colloquium at MIT. This was also my term paper for 18.434, “Seminar in Theoretical Computer Science”.

1. Introducing the choice number

One of the most fundamental problems in graph theory is that of a graph coloring, in which one assigns a color to every vertex of a graph so that no two adjacent vertices have the same color. The most basic invariant related to the graph coloring is the chromatic number:

Definition 1. A simple graph GG is kk-colorable if it’s possible to properly color its vertices with kk colors. The smallest such kk is the chromatic number χ(G)\chi(G).

In this exposition we study a more general notion in which the set of permitted colors is different for each vertex, as long as at least kk colors are listed at each vertex. This leads to the notion of a so-called choice number, which was introduced by Erdös, Rubin, and Taylor.

Definition 2. A simple graph GG is kk-choosable if its possible to properly color its vertices given a list of kk colors at each vertex. The smallest such kk is the choice number ch(G)\operatorname{ch}(G).

Example 3. We have ch(C2n)=χ(C2n)=2\operatorname{ch}(C_{2n}) = \chi(C_{2n}) = 2 for any integer nn (here C2nC_{2n} is the cycle graph on 2n2n vertices). To see this, we only have to show that given a list of two colors at each vertex of C2nC_{2n}, we can select one of them.

  • If the list of colors is the same at each vertex, then since C2nC_{2n} is bipartite, we are done.
  • Otherwise, suppose adjacent vertices v1v_1, v2nv_{2n} are such that some color at cc is not in the list at v2nv_{2n}. Select cc at v1v_1, and then greedily color in v2v_2, …, v2nv_{2n} in that order.

We are thus naturally interested in how the choice number and the chromatic number are related. Of course we always have ch(G)χ(G).\operatorname{ch}(G) \ge \chi(G). Näively one might expect that we in fact have an equality, since allowing the colors at vertices to be different seems like it should make the graph easier to color. However, the following example shows that this is not the case.

Example 4 (Erdös)

Let n1n \ge 1 be an integer and define G=Knn,n.G = K_{n^n, n}. We claim that for any integer n1n \ge 1 we have ch(G)n+1andχ(G)=2.\operatorname{ch}(G) \ge n+1 \quad\text{and}\quad \chi(G) = 2. The latter equality follows from GG being partite.

Now to see the first inequality, let GG have vertex set UVU \cup V, where UU is the set of functions u:[n][n]u : [n] \rightarrow [n] and V=[n]V = [n]. Then consider n2n^2 colors Ci,jC_{i,j} for 1i,jn1 \le i, j \le n. On a vertex uUu \in U, we list colors C1,u(1)C_{1,u(1)}, C2,u(2)C_{2,u(2)}, …, Cn,u(n)C_{n,u(n)}. On a vertex vVv \in V, we list colors Cv,1C_{v,1}, Cv,2C_{v,2}, …, Cv,nC_{v,n}. By construction it is impossible to properly color GG with these colors.

The case n=3n = 3 is illustrated in the figure below (image in public domain).

The n=3 case showing choice numbers and chromatic numbers can differ.
The n=3 case showing choice numbers and chromatic numbers can differ.

This surprising behavior is the subject of much research: how can we bound the choice number of a graph as a function of its chromatic number and other properties of the graph? We see that the above example requires exponentially many vertices in nn.

Theorem 5 (Noel, West, Wu, Zhu)

If GG is a graph with nn vertices then

χ(G)ch(G)max(χ(G),χ(G)+n13). \chi(G) \le \operatorname{ch}(G) \le \max\left( \chi(G), \left\lceil \frac{\chi(G)+n-1}{3} \right\rceil \right).

In particular, if n2χ(G)+1n \le 2\chi(G)+1 then ch(G)=χ(G)\operatorname{ch}(G) = \chi(G).

One of the most major open problems in this direction is the following.

Definition 6. A claw-free graph is a graph with no induced K3,1K_{3,1}. For example, the line graph (also called edge graph) of any simple graph GG is claw-free.

If GG is a claw-free graph, then it’s conjectured ch(G)=χ(G)\operatorname{ch}(G) = \chi(G). In particular, this conjecture implies that for edge coloring, the notions of “chromatic number” and “choice number” coincide.

In this exposition, we prove the following result of Alon.

Theorem 7 (Alon)

A bipartite graph GG is L(G)+1\left\lfloor L(G) \right\rfloor+1 choosable, where L(G)=defmaxHGE(H)/V(H)L(G) \overset{\mathrm{def}}{=} \max_{H \subseteq G} |E(H)|/|V(H)| is half the maximum of the average degree of subgraphs HH.

In particular, recall that a planar bipartite graph HH with rr vertices contains at most 2r42r-4 edges. Thus for such graphs we have L(G)2L(G) \le 2 and deduce:

Corollary 8. A planar bipartite graph is 33-choosable.

This corollary is sharp, as it applies to K2,4K_{2,4} which we have seen in Example 4 has ch(K2,4)=3\operatorname{ch}(K_{2,4}) = 3.

The rest of the paper is divided as follows. First, we begin in §2 by stating Theorem 9, the famous combinatorial nullstellensatz of Alon. Then in §3 and §4, we provide descriptions of the so-called graph polynomial, to which we then apply combinatorial nullstellensatz to deduce Theorem 18. Finally in §5, we show how to use Theorem 18 to prove Theorem 7.

2. Combinatorial Nullstellensatz

The main tool we use is the Combinatorial Nullestellensatz of Alon.

Theorem 9 (Combinatorial Nullstellensatz)

Let FF be a field, and let fF[x1,,xn]f \in F[x_1, \dots, x_n] be a polynomial of degree t1++tnt_1 + \dots + t_n. Let S1,S2,,SnFS_1, S_2, \dots, S_n \subseteq F such that Si>ti\left\lvert S_i \right\rvert > t_i for all ii.

Assume the coefficient of x1t1x2t2xntnx_1^{t_1}x_2^{t_2}\dots x_n^{t_n} of ff is not zero. Then we can pick s1S1s_1 \in S_1, …, snSns_n \in S_n such that f(s1,s2,,sn)0.f(s_1, s_2, \dots, s_n) \neq 0.

Example 10. Let us give a second proof that ch(C2n)=2\operatorname{ch}(C_{2n}) = 2 for every positive integer nn. Our proof will be an application of the Nullstellensatz.

Regard the colors as real numbers, and let SiS_i be the set of colors at vertex ii (hence 1i2n1 \le i \le 2n, and Si=2|S_i| = 2). Consider the polynomial

f=(x1x2)(x2x3)(x2n1x2n)(x2nx1) f = \left( x_1-x_2 \right)\left( x_2-x_3 \right) \dots \left( x_{2n-1}-x_{2n} \right)\left( x_{2n}-x_1 \right)

The coefficient of x11x21x2n1x_1^1 x_2^1 \dots x_{2n}^1 is 202 \neq 0. Therefore, one can select a color from each SiS_i so that ff does not vanish.

3. The Graph Polynomial, and Directed Orientations

Motivated by Example 10, we wish to apply a similar technique to general graphs GG. So in what follows, let GG be a (simple) graph with vertex set {1,,n}\{1, \dots, n\}.

Definition 11. The graph polynomial of GG is defined by fG(x1,,xn)=(i,j)E(G)i<j(xixj).f_G(x_1, \dots, x_n) = \prod_{\substack{(i,j) \in E(G) \\ i < j}} (x_i-x_j).

We observe that coefficients of fGf_G correspond to differences in directed orientations. To be precise, we introduce the notation:

Definition 12. Consider orientations on the graph GG with vertex set {1,,n}\{1, \dots, n\}, meaning we assign a direction vwv \rightarrow w to every edge of GG to make it into a directed graph GG. An oriented edge is called ascending if vwv \rightarrow w and vwv \le w, i.e. the edge points from the smaller number to the larger one.

Then we say that an orientation is

  • even if there are an even number of ascending edges, and
  • odd if there are an odd number of ascending edges.

Finally, we define

  • DEG(d1,,dn)\mathop{\mathrm{DE}}_G(d_1, \dots, d_n) to the be set of all even orientations of GG in which vertex ii has indegree did_i.
  • DOG(d1,,dn)\mathop{\mathrm{DO}}_G(d_1, \dots, d_n) to the be set of all odd orientations of GG in which vertex ii has indegree did_i.

Set DG(d1,,dn)=DEG(d1,,dn)DOG(d1,,dn)\mathop{\mathrm{D}}_G(d_1,\dots,d_n) = \mathop{\mathrm{DE}}_G(d_1,\dots,d_n) \cup \mathop{\mathrm{DO}}_G(d_1,\dots,d_n).

Example 13. Consider the following orientation:

An even orientation.
An even orientation.

There are exactly two ascending edges, namely 121 \rightarrow 2 and 242 \rightarrow 4. The indegrees of are d1=0d_1 = 0, d2=2d_2 = 2 and d3=d4=1d_3 = d_4 = 1. Therefore, this particular orientation is an element of DEG(0,2,1,1)\mathop{\mathrm{DE}}_G(0,2,1,1). In terms of fGf_G, this corresponds to the choice of terms

(x1x2)(x2x3)(x2x4)(x3x4) \left( x_1- \boldsymbol{x_2} \right) \left( \boldsymbol{x_2}-x_3 \right) \left( x_2-\boldsymbol{x_4} \right) \left( \boldsymbol{x_3}-x_4 \right)

which is a +x22x3x4+ x_2^2 x_3 x_4 term.

Lemma 14. In the graph polynomial of GG, the coefficient of x1d1xndnx_1^{d_1} \dots x_n^{d_n} is

DEG(d1,,dn)DOG(d1,,dn). \left\lvert \mathop{\mathrm{DE}}_G(d_1, \dots, d_n) \right\rvert - \left\lvert \mathop{\mathrm{DO}}_G(d_1, \dots, d_n) \right\rvert.

Proof: Consider expanding fGf_G. Then each expanded term corresponds to a choice of xix_i or xjx_j from each (i,j)(i,j), as in Example 13. The term has coefficient +1+1 is the orientation is even, and 1-1 if the orientation is odd, as desired. \Box

Thus we have an explicit combinatorial description of the coefficients in the graph polynomial fGf_G.

4. Coefficients via Eulerian Suborientations

We now give a second description of the coefficients of fGf_G.

Definition 15. Let DDG(d1,,dn)D \in \mathop{\mathrm{D}}_G(d_1, \dots, d_n), viewed as a directed graph. An Eulerian suborientation of DD is a subgraph of DD (not necessarily induced) in which every vertex has equal indegree and outdegree. We say that such a suborientation is

  • even if it has an even number of edges, and
  • odd if it has an odd number of edges.

Note that the empty suborientation is allowed. We denote the even and odd Eulerian suborientations of DD by EE(D)\mathop{\mathrm{EE}}(D) and EO(D)\mathop{\mathrm{EO}}(D), respectively.

Eulerian suborientations are brought into the picture by the following lemma.

Lemma 16. Assume DDEG(d1,,dn)D \in \mathop{\mathrm{DE}}_G(d_1, \dots, d_n). Then there are natural bijections

DEG(d1,,dn)EE(D)DOG(d1,,dn)EO(D). \begin{aligned} \mathop{\mathrm{DE}}_G(d_1, \dots, d_n) &\rightarrow \mathop{\mathrm{EE}}(D) \\ \mathop{\mathrm{DO}}_G(d_1, \dots, d_n) &\rightarrow \mathop{\mathrm{EO}}(D). \end{aligned}

Similarly, if DDOG(d1,,dn)D \in \mathop{\mathrm{DO}}_G(d_1, \dots, d_n) then there are bijections

DEG(d1,,dn)EO(D)DOG(d1,,dn)EE(D). \begin{aligned} \mathop{\mathrm{DE}}_G(d_1, \dots, d_n) &\rightarrow \mathop{\mathrm{EO}}(D) \\ \mathop{\mathrm{DO}}_G(d_1, \dots, d_n) &\rightarrow \mathop{\mathrm{EE}}(D). \end{aligned}

Proof: Consider any orientation DDG(d1,,dn)D' \in \mathop{\mathrm{D}}_G(d_1, \dots, d_n). Then we define a suborietation of DD, denoted DDD \rtimes D', by including exactly the edges of DD whose orientation in DD' is in the opposite direction. It’s easy to see that this induces a bijection

D:DG(d1,,dn)EE(D)EO(D) D \rtimes - : \mathop{\mathrm{D}}_G(d_1, \dots, d_n) \rightarrow \mathop{\mathrm{EE}}(D) \cup \mathop{\mathrm{EO}}(D)

Moreover, remark that

  • DDD \rtimes D' is even if DD and DD' are either both even or both odd, and
  • DDD \rtimes D' is odd otherwise.

The lemma follows from this. \Box

Corollary 17. In the graph polynomial of GG, the coefficient of x1d1xndnx_1^{d_1} \dots x_n^{d_n} is

±(EE(D)EO(D)) \pm \left( \left\lvert \mathop{\mathrm{EE}}(D) \right\rvert - \left\lvert \mathop{\mathrm{EO}}(D) \right\rvert \right)

where DDG(d1,,dn)D \in \mathop{\mathrm{D}}_G(d_1, \dots, d_n) is arbitrary.

Proof: Combine Lemma 14 and Lemma 16. \Box

We now arrive at the main result:

Theorem 18. Let GG be a graph on {1,,n}\{1, \dots, n\}, and let DDG(d1,,dn)D \in \mathop{\mathrm{D}}_G(d_1, \dots, d_n) be an orientation of GG. If EE(D)EO(D)\left\lvert \mathop{\mathrm{EE}}(D) \right\rvert \neq \left\lvert \mathop{\mathrm{EO}}(D) \right\rvert, then given a list of di+1d_i+1 colors at each vertex of GG, there exists a proper coloring of the vertices of GG.

In particular, GG is (1+maxidi)(1+\max_i d_i)-choosable.

Proof: Combine Corollary 17 with Theorem 9. \Box

5. Finding an orientation

Armed with Theorem 18, we are almost ready to prove Theorem 7. The last ingredient is that we need to find an orientation on GG in which the maximal degree is not too large. This is accomplished by the following.

Lemma 19. Let L(G)=defmaxHGE(H)/V(H)L(G) \overset{\mathrm{def}}{=} \max_{H \subseteq G} |E(H)|/|V(H)| as in Theorem 7. Then GG has an orientation in which every indegree is at most L(G)\left\lceil L(G) \right\rceil.

Proof: This is an application of Hall’s marriage theorem.

Let d=L(G)L(G)d = \left\lceil L(G) \right\rceil \ge L(G). Construct a bipartite graph

EXwhereE=E(G) and X=V(G)V(G)d times. E \cup X \qquad \text{where}\qquad E = E(G) \quad\text{ and }\quad X = \underbrace{V(G) \sqcup \dots \sqcup V(G)}_{d \text{ times}}.

Connect eEe \in E and vXv \in X if vv is an endpoint of ee. Since dL(G)d \ge L(G) we satisfy Hall’s condition (as L(G)L(G) is a condition for all subgraphs HGH \subseteq G) and can match each edge in EE to a (copy of some) vertex in XX. Since there are exactly dd copies of each vertex in XX, the conclusion follows. \Box

Now we can prove Theorem 7.

Proof: According to Lemma 19, pick DDG(d1,,dn)D \in \mathop{\mathrm{D}}_G(d_1, \dots, d_n) where maxdiL(G)\max d_i \le \left\lceil L(G) \right\rceil. Since GG is bipartite, we obviously have EO(D)=\mathop{\mathrm{EO}}(D) = \varnothing, since GG cannot have any odd cycles. So Theorem 18 applies and we are done. \Box