Chromatic number of the plane

The unit distance graph on \mathbb R^2 has edges between those pairs of points at Euclidean distance 1.  The chromatic number of this graph lies between 4 (by exhibiting a small subgraph on 7 vertices with chromatic number 4) and 7 (by an explicit colouring based on a hexagonal tiling of the plane).  Aubrey de Grey has just posted a construction of a unit distance graph with chromatic number 5, raising the lower bound on \chi(\mathbb R^2) by 1This MathOverflow post is a good jumping off point into the discussion online.

I was explaining this problem to a colleague and they asked whether this graph was connected (it is) and whether that was still true if we restricted to rational coordinates.  It turns out this was addressed by Kiran B.Chilakamarri in 1988, and the answer is the rational unit distance graph is connected from dimension 5 onwards.

To see that \mathbb Q^4 is not connected, consider a general unit vector x = (a_1/b, a_2/b, a_3/b, a_4/b) where b is coprime to \gcd(a_1, a_2, a_3, a_4).  Then a_1^2 + a_2^2 + a_3^2 + a_4^2 = b^2.

Claim.  b is divisible by 2 at most once.

Proof. Squares mod 8 are either 0, 1 or 4.  If b is divisible by 4 then one of the a_i is odd, hence squares to 1 mod 8.  But then a_1^2 + a_2^2 + a_3^2 + a_4^2 cannot be divisible by 8, which is a contradiction.

So the entries of x in their reduced form do not contain any 4‘s in their denominator, and so the same must hold for all sums of unit vectors.  Hence we can’t express, say, (1/4, 0, 0, 0) as a sum of unit vectors, and (1/4, 0, 0, 0) is not connected to 0.

Connectedness in dimension 5 (hence also later) uses Lagrange’s theorem on the sums of four squares.  We’ll show that (1/N, 0, 0, 0, 0) can be expressed as a sum of 2 unit vectors.  By Lagrange’s theorem, write 4N^2-1 = a_1^2 + a_2^2 + a_3^2 + a_4^2.  Then

    \[1 = \left(\frac 1 {2N}\right)^2 + \left(\frac {a_1} {2N}\right)^2+ \left(\frac {a_2} {2N}\right)^2+ \left(\frac {a_3} {2N}\right)^2+ \left(\frac {a_4} {2N}\right)^2\]


    \[\left(\frac 1 {N},0,0,0,0\right) = \left(\frac 1 {2N},\frac {a_1} {2N},\frac {a_2} {2N},\frac {a_3} {2N},\frac {a_4} {2N}\right)+ \left(\frac 1 {2N}, -\frac {a_1} {2N}, -\frac {a_2} {2N}, -\frac {a_3} {2N}, -\frac {a_4} {2N}\right)\]

is a sum of 2 unit vectors.

The number of maximal left-compressed intersecting families

A family of sets \mathcal A \subseteq [n]^{(r)} (subsets of \{1, \ldots, n\} of size r) is intersecting if every pair of sets in \mathcal A have a common element.  If n < 2r then every pair of sets intersect, so |\mathcal A| can be as large as \binom n r.  If n \geq 2r then the Erdős–Ko–Rado theorem states that |\mathcal A| \leq \binom {n-1} {r-1}, which (up to relabelling of the ground set) is attained only by the star \mathcal S of all sets containing the element 1.

A hands on proof of the Erdős–Ko–Rado theorem use a tool called compression.  A family \mathcal A is left-compressed if for every A \in \mathcal A, any set obtained from A by deleting an element and replacing it by a smaller one is also in \mathcal A.  You can show by repeatedly applying a certain compression operator that for every intersecting family \mathcal A there is a left-compressed intersecting family \mathcal A' of the same size.  Thus it suffices to prove the Erdős–Ko–Rado theorem for left-compressed families, which is easy to do by induction.

There is a strong stability result for large intersecting families.  The Hilton–Milner family consists of all sets that contain 1 and at least one element of [2,r+1], together with [2,r+1] itself.  This is an intersecting family, and in fact is the largest intersecting family not contained in a star.  The Hilton–Milner family has size O(n^{r-2}), so any family that gets anything like close to the Erdős–Ko–Rado bound must be a subset of a star.

As part of an alternative proof of the Hilton–Milner theorem, Peter Borg partially answered the following question.

Let \mathcal A \subseteq [n]^{(r)} be an intersecting family and let X \subseteq [n].  Let \mathcal A(X) = \{A \in \mathcal A : A \cap X \neq \emptyset\}.  For which X is |\mathcal A(X)| \leq |\mathcal S(X)|?

Borg used that fact that this is true for X = [2,r+1] to reprove the Hilton–Milner theorem.  In Maximum hitting for n sufficiently large I completed the classification of X for which this is true for large n.  The proof used the apparently new observation that, for n \geq 2r, every maximal left-compressed intersecting family in [n]^{(r)} corresponds to a unique maximal left-compressed intersecting family of [2r]^{(r)}.  In particular, the number of maximal left-compressed intersecting families for n \geq 2r is independent of n.  For r=1, 2, 3, 4, 5, 6 there are 1, 2, 6, 72, 37145, 1081162102034 such families respectively.  In the rest of this post I’ll explain how I obtained these numbers.

We want to count maximal left-compressed intersecting families of [2r]^{(r)}.  The maximal part is easy: the only way to get two disjoint sets of size r from [2r] is to take a set and its complement, so we must simply choose one set from each complementary pair.  To make sure the family we generate in this way is left-compressed we must also ensure that whenever we choose a set A we must also choose every set B with B \leq A, where B \leq A means “B can be obtained from A by a sequence of compressions”.  The compression order has the following properties.

  • If A = \{a_1 < \cdots < a_r\} and B = \{b_1 < \cdots < b_r\} then A \leq B if and only if a_i \leq b_i for each i.
  • A \leq B if and only if B^c \leq A^c.

Here’s one concrete algorithm.

  1. Generate a list of all sets from [2r-1]^{(r)}.  This list has one set from each complementary pair.
  2. Put all A from the list with A < A^c into \mathcal A.  (These sets must be in every maximal left-compressed intersecting family.)  Note that we never have A^c < A as A^c contains 2r but A doesn’t.
  3. Let A be the first element of the list and branch into two cases depending on whether we take A or A^c.
    • If we take A, also take all B from the list with B < A and B^c for all B from the list with B^c < A. (Since B^c contains 2r and A doesn’t, the second condition will never actually trigger.)
    • If we take A^c, also take all B from the list with B < A^c and B^c for all B from the list with B^c < A^c. It is cheaper to test A < B than the second condition to avoid taking complements.
  4. Repeat recursively on each of the two lists generated in the previous step.  Stop on each branch whenever the list of remaining options is empty.

The following is a fairly direct translation of this algorithm into Haskell that makes no attempt to store the families generated and just counts the number of possibilities.  A source file with the necessary import’s and the choose function is attached to the end of this post.

r = 5

simpleOptions = [a | a <- choose r [1..(2*r-1)], not [dollar-sign] a `simpleLeftOf` (simpleComplement a)]

simpleLeftOf xs ys = all id [dollar-sign] zipWith (<=) xs ys

simpleComplement a = [1..(2*r)] \\ a

simpleCount [] = 1
simpleCount (a:as) = simpleCount take + simpleCount leave
    -- take a
    -- all pairs with b < a or b^c < a are forced
    -- second case never happens as b^c has 2r but a doesn't
    take = [b | b <- as, not [dollar-sign] b `simpleLeftOf` a]
    -- leave a, and so take a^c
    -- all pairs with b < a^c or b^c < a^c (equivalently, a < b) are forced
    c = simpleComplement a
    leave = [b | b <- as, not (b `simpleLeftOf` c || a `simpleLeftOf` b)]

This will compute the number of maximal left-compressed intersecting families for r \leq 5 in a fraction of a second.  For r=6 it would probably find the answer in less than a month.  I obtained the value for r=6 in a couple of days on a single core by using a better representation of the sets in our family.

The dream is to pack all of the elements of our list into a single machine word and perform each comparison in a small number of instructions.  For example, we could encode an element of [12]^{(6)} by writing each element as 4 binary digits then concatenating them in increasing order to obtain a 24 bit word.  But comparing two such words as integers compares the corresponding sets lexicographically rather than pointwise.  Edward Crane suggested that as the lists are so short and the elements are so small we can afford to be quite a lot more wasteful in our representation: we can write each element of our set in unary!  The rest of this section should be considered joint work with him.

The first iteration of the idea is to write each element x of [12] as a string of x 1’s followed by 12-x 0’s, then concatenate these strings to obtain a representation of our set.  This representation has the great advantage that we can compare sets pointwise by comparing strings bitwise, and we can do this using very few binary operations: a is contained in b if and only if a \& b = a.

Unfortunately this representation uses 72 bits in total, so won’t fit into a 64-bit machine word. Observing that we never use 0 and encoding by x-1 1‘s followed by 11-x 0‘s saves only 6 bits. But we can do even better by encoding each element of the set differently. The first element is always at least 1, the second is always at least 2 and so on. Similarly, the first element is at most 7, the second at most 8 and so on. Working through the details we arrive at the following representation.

Identify each element of [12]^{(6)} by an “up-and-right” path from the bottom left to the top right corner of a 6 \times 6 grid: at the ith step move right if i is in your set and up if it isn’t. Then A \leq B if and only if the path corresponding to A never goes below the path corresponding to B. So we can compare sets by comparing the regions below the corresponding paths. Recording these regions can be done using 36 bits, which happily sits inside a machine word. This representation also has the helpful property that taking the complement of a set corresponds to reflecting a path about the up-and-right diagonal, so the representation of the complement of a set can be obtained by swapping certain pairs of bits followed by a bitwise NOT.

The value for r=6 was obtained using this new representation and the old algorithm, with one minor tweak.  It’s a bad idea to start with a lexicographically ordered list of sets, as the early decisions will not be meaningful and not lead to much of a reduction in the length of the the lists.  Optimal selection of which pair to decide at each stage is probably a complicated question.  As a compromise I randomised the order of the list at the start of the process, then took the first remaining pair at each stage.

The Haskell source is here.  There are a few more performance tricks to do with the exact bit representation of the sets, which I’m happy to discuss if anything is unclear.

Counting colourings with containers

On the maximum number of integer colourings with forbidden monochromatic sums, Hong Liu, Maryam Sharifzadeh and Katherine Staden

Maryam spoke about this paper at this week’s combinatorics seminar.

The problem is as follows.  Let f(A, r) be the number of r-colourings of a subset A of [n] with no monochromatic sum x + y = z.  What is the maximum f(n,r) of f(A, r) over all A \subseteq [n]?

One possibility is that we take A to be sum-free, so that f(A, r) = r^{|A|}.  The maximum size of a sum-free set of [n] is around n/2, achieved by the set O of odd numbers and the interval I = [\lfloor n/2 \rfloor + 1, n], so f(n, r) \geq r^{n/2}.

Another possibility is to choose r sum-free sets A_1, \ldots, A_r and take all colourings of A = A_1 \cup \cdots \cup A_r such that the elements of colour i are contained in A_i.  There are

    \[1^{n_1} \cdot 2^{n_2}  \cdots  r^{n_r}\]

such colourings, where n_j is the number of elements in exactly j of the A_i.  For example, we might take half of the A_i to be O and half to be I.  Then the odd numbers greater than n/2 are in every set, and the evens greater than n/2 and the odds less than n/2 are in half of the sets, so the number of colourings is around


For r=4 this matches the previous lower bound; for r \geq 5 it is larger.

It’s easy to see that this construction cannot improve the bound for r = 2: it only provides 2^{n_2} good colourings, but n_2 \leq n/2 as elements contributing to n_2 are in A_1 \cap A_2, which must be sum-free.

What about r=3?  Now we get 2^{n_2}3^{n_3} = 3^{n^3 + n_2 / \log_2 3} good colourings.  We also have that

    \[2n_2 + 3n_3 \leq  |A_1| + |A_2| + |A_3| \leq 3n/2.\]

But since \log_2(3) > 3/2 we have

    \[3^{n^3 + n_2 / \log_2 3} \leq 3^{n^3 + 2n_2 / 3} \leq 3^{n/2}.\]

Moreover, if n_2 is not tiny then we are some distance off this upper bound, so the only good constructions in this family come from having all the A_i substantially agree.

How can we get matching upper bounds?  If there weren’t very many maximal sum-free sets then we could say that every good colouring arises from a construction like this, and there aren’t too many such constructions to consider.  This is too optimistic, but the argument can be patched up using containers.

The container method is a relatively recent addition to the combinatorial toolset.  For this problem the key fact is that there is a set \mathcal F of 2^{o(n)} subsets of [n] such that

  • every sum-free set is contained in some F \in \mathcal F,
  • each F \in \mathcal F is close to sum-free.  Combined with a “sum-removal lemma” this means in particular that it has size not much larger than n/2.

We now consider running the above construction with each A_i an element of \mathcal F.  Since the containers are not themselves sum-free, this will produce some bad colourings.  But because every sum-free set is contained in some element of \mathcal F, every good colouring of a subset A of [n] will arise in this way.  And since there are most |\mathcal F|^r choices for the sets A_i the number of colourings we produce is at most a factor 2^{o(n)} greater than the biggest single example arising from the construction.

This is the big idea of the paper: it reduces counting colourings to the problem of optimising this one construction.  For r \leq 5 the authors are able to solve this new problem, and so the original.


Linear programming duality

The conventional statement of linear programming duality is completely inscrutable.

  • Prime: maximise b^T x subject to Ax \leq c and x \geq 0.
  • Dual: minimise c^T y subject to A^T y \leq b and y \geq 0.

If either problem has a finite optimum then so does the other, and the optima agree.

do understand concrete examples.  Suppose we want to pack the maximum number vertex-disjoint copies of a graph F into a graph G.  In the fractional relaxation, we want to assign each copy of F a weight \lambda_F \in [0,1] such that the weight of all the copies of F at each vertex is at most 1, and the total weight is as large as possible.  Formally, we want to

maximise \sum \lambda_F subject to \sum_{F \ni v} \lambda_F \leq 1 and \lambda_F \geq 0,

which dualises to

minimise \sum \mu_v subject to \sum_{v \in F} \mu_v \geq 1 and \mu_v \geq 0.

That is, we want to weight vertices as cheaply as possible so that every copy of F contains 1 (fractional) vertex.

To get from the prime to the dual, all we had to was change a max to a min, swap the variables indexed by F for variables indexed by v and flip one inequality.  This is so easy that I never get it wrong when I’m writing on paper or a board!  But I thought for years that I didn’t understand linear programming duality.

(There are some features of this problem that make things particularly easy: the vectors b and c in the conventional statement both have all their entries equal to 1, and the matrix A is 0/1-valued.  This is very often the case for problems coming from combinatorics.  It also matters that I chose not to make explicit that the inequalities should hold for every v (or F, as appropriate).)

Returning to the general statement, I think I’d be happier with

  • Prime: maximise \sum_j b_j x_j subject to \sum_j A_{ij}x_j \leq c_i and x \geq 0.
  • Dual: minimise \sum_i y_i subject to \sum_i A_{ij} y_i \leq b_j and y \geq 0.

My real objection might be to matrix transposes and a tendency to use notation for matrix multiplication just because it’s there.  In this setting a matrix is just a function that takes arguments of two different types (v and F or, if you must, i and j), and I’d rather label the types explicitly than rely on an arbitrary convention.

Random Structures and Algorithms 2017

A partial, chronologically ordered, list of talks I attended at RSA in Gniezno, Poland. Under construction until the set of things I can remember equals the set of things I’ve written about.

Shagnik Das

A family of subsets of [n] that shatters a k-set has at least 2^k elements. How many k-sets can we shatter with a family of size 2^k? A block construction achieves (n/k)^k \approx e^{-k} \binom n k. (Random is much worse.) Can in fact shatter constant fraction of all k-sets. When n = 2^k-1, identify the ground set with \mathbb F_2^k \setminus \{0\}, and colour by \chi_w(v) = v \cdot w for w \in \mathbb F_2^k.

Claim. A k-set is shattered if and only if it is a basis for \mathbb F_2^k.

Proof. First suppose that v_1, \ldots, v_k is a basis. Then for any sequence \epsilon_i, there is a unique vector w such that v_i \cdot w = \epsilon_i. (We are just solving a system of full rank equations mod 2.)

Next suppose that v_1, \ldots, v_k are linearly dependent; that is, that they are contained in a subspace U of \mathbb F_2^k. Choose w orthogonal to U. Then for any u \in U and any w' we have u \cdot w' = u \cdot (w+w'), so two of our colourings agree on v_1, \ldots, v_k. \Box

We finish with the observation that random sets of k vectors are fairly likely to span \mathbb F_2^k: the probability is

    \[ 1 \cdot (1 - 1/2^k) \cdot (1 - 1/2^{k-1}_ \cdot \cdots \cdot (1-1/2) \geq 1 - \sum_j=1^k 1/2^j > 0. \]

Blowing up this colouring gives a construction that works for larger n.

At the other end of the scale, we can ask how large a family is required to shatter every k-set from [n]. The best known lower bound is \Omega(2^k \log n), and the best known upper bound is O(k2^k \log n), which comes from a random construction. Closing the gap between these bounds, or derandomising the upper bound, would both be of significant interest.

Andrew McDowell

At the start of his talk in Birmingham earlier this summer, Peter Hegarty played two clips from Terminator in which a creature first dissolved into liquid and dispersed, then later reassembled, stating that it had prompted him to wonder how independent agents can meet up without any communication. Andrew tackled the other half of this question: how can non-communicating agents spread out to occupy distinct vertices of a graph? He was able to analyse some strategies using Markov chains in a clever way.

Tássio Naia

A sufficient condition for embedding an oriented tree on n vertices into every tournament on n vertices that implies that almost all oriented trees are unavoidable in this sense.

Matchings without Hall’s theorem

In practice matchings are found not by following the proof of Hall’s theorem but by starting with some matching and improving it by finding augmenting paths.  Given a matching M in a bipartite graph on vertex classes X and Y, an augmenting path is a path P from x \in X \setminus V(M) to y \in Y \setminus V(M) such that ever other edge of P is an edge of M.  Replace P \cap M by P \setminus M produces a matching M' with |M'| = |M| + 1.

Theorem.  Let G be a spanning subgraph of K_{n,n}.  If (i) \delta(G) \geq n/2 or (ii) G is k-regular, then G has a perfect matching.

Proof. Let M = \{x_1y_1, \ldots, x_ty_t\} be a maximal matching in G with V(M) \subset V(G).

(i) Choose x \in X \setminus V(M), y \in Y \setminus V(M).  We have N(x) \subseteq V(M) \cap Y and N(y) \subseteq V(M) \cap X.  Since \delta(G) \geq n/2 there is an i such that x is adjacent to y_i and y is adjacent to x_i.  Then xy_ix_iy is an augmenting path.

(ii) Without loss of generality, G is connected.  Form the directed graph D on v_1, \ldots, v_t by taking the directed edge \vec{v_iv_j}  (i \neq j) whenever x_iy_j is an edge of G.  Add directed edges arbitrarily to D to obtain a k-regular digraph D', which might contain multiple edges; since G is connected we have to add at least one directed edge.  The edge set of D' decomposes into directed cycles.  Choose a cycle C containing at least one new edge of D', and let P be a maximal sub-path of C containing only edges of D.  Let v_i, v_j be the start- and endpoints of P respectively.  Then we can choose y \in N(x_i) \setminus V(M) and x \in N(y_j) \setminus V(M), whence yQx is an augmenting path, where Q is the result of “pulling back” P from D to G, replacing each visit to a v_s in D by use of the edge y_sx_s of G. \square


Matchings and minimum degree

A Tale of Two Halls

(Philip) Hall’s theorem.  Let G be a bipartite graph on vertex classes X, Y.  Suppose that,  for every S \subseteq X, |N(S)| \geq |S|.  Then there is a matching from X to Y.

This is traditionally called Hall’s marriage theorem.  The picture is that the people in X are all prepared to marry some subset of the people in Y.  If some k people in X are only prepared to marry into some set of k-1 people, then we have a problem; but this is the only problem we might have.  There is no room in this picture for the preferences of people in Y.

Proof. Suppose first that |N(S)| > |S| for all S \subset X.  Then we can match any element of X arbitrarily to a neighbour in Y and obtain a smaller graph on which Hall’s condition holds, so are done by induction.

Otherwise |N(S)| = |S| for some S \subset X.  By induction there is a matching from S to N(S).  Let T = X \setminus S.  Then for any U \subseteq T we have

    \[|N(U) \setminus N(S)| + |N(S)| = |N(U \cup S)| \geq |U \cup S| = |U| + |S|\]

hence |N(U) \setminus N(S)| \geq |U| and Hall’s condition holds on (T, Y \setminus N(S)).  So by induction there is a matching from T to Y \setminus N(S), which together with the matching from S to N(S) is a matching from X to Y. \square

Corollary 1.  Every k-regular bipartite graph has a perfect matching.

Proof. Counted with multiplicity, |N(S)| = k|S|.  But each element of Y is hit at most k times, so |N(S)| \geq k|S| / k = |S| in the conventional sense. \square

Corollary 2. Let G be a spanning subgraph of K_{n,n} with minimum degree at least n/2.  Then G has a perfect matching.

This is very slightly more subtle.

Proof. If |N(S)| = n there is nothing to check.  Otherwise there is a y \in Y \setminus N(S).  Then N(y) \subseteq X \setminus S and |N(y)| \geq n/2, so |S| \leq n/2.  But |N(S)| \geq n/2 for every S. \square

Schrijver proved that a k-regular bipartite graph with n vertices in each class in fact has at least \left(\frac {(k-1)^{k-1}} {k^{k-2}}\right)^n perfect matchings.  For minimum degree k we have the following.

(Marshall) Hall’s theorem.  If each vertex of X has degree at least k and there is at least one perfect matching then there are at least k!.

This turns out to be easy if we were paying attention during the proof of (Philip) Hall’s theorem.

Proof. By (Philip) Hall’s theorem the existence of a perfect matching means that (Philip) Hall’s condition holds.  Choose a minimal S on which it is tight.  Fix x \in S and match it arbitrarily to a neighbour y \in Y.  (Philip) Hall’s condition still holds on (S - x, Y-y) and the minimum degree on this subgraph is at least k-1, so by induction we can find at least (k-1)! perfect matchings.  Since there were at least k choices for y we have at least k! perfect matchings from S to N(S).  These extend to perfect matchings from X to Y as in the proof of (Philip) Hall’s theorem. \square

Thanks to Gjergji Zaimi for pointing me to (Marshall) Hall’s paper via MathOverflow.

Block partitions of sequences

Let L be a line segment of length l broken into pieces of length at most 1. It’s easy to break L into k blocks (using the preexisting breakpoints) that differ in length by at most 2 (break at the nearest available point to l/k, 2l/k etc.).

In the case where each piece has length 1 and the number of pieces isn’t divisible by k, we can’t possibly do better than a maximum difference of 1 between blocks.  Is this achievable in general?

In today’s combinatorics seminar Imre Bárány described joint work with Victor Grinberg which says that the answer is yes.

Continue reading “Block partitions of sequences”

The Namer-Claimer game

Consider the following game played on the integers from 1 to n.  In each round Namer names a forbidden distance d, then Claimer claims a subset of [n] that does not contain any two integers at distance d.  After finitely many rounds, Claimer will have claimed sets that cover the whole of [n], at which point the game ends.  How many rounds will there be with best play?

I’ve run this question out at a number of workshops and open problems sessions, and haven’t yet heard back about a success.  I’ll explain the known upper and lower bounds below the fold, but encourage you to spend a few minutes thinking about it before taking a look.

Continue reading “The Namer-Claimer game”

Nowhere zero 6-flows

A flow on a graph G is an assignment to each edge of G of a direction and a non-negative integer (the flow in that edge) such that the flows into and out of each vertex agree.  A flow is nowhere zero if every edge is carrying a positive flow and (confusingly) it is a k-flow if the flows on each edge are all less than k.  Tutte conjectured that every bridgeless graph has a nowhere zero 5-flow (so the possible flow values are 1, 2, 3, 4).  This is supposed to be a generalisation of the 4-colour theorem.  Given a plane graph G and a proper colouring of its faces by 1, 2, 3, 4, push flows of value i anticlockwise around each face of G.  Adding the flows in the obvious way gives a flow on G in which each edge has a total flow of |i-j| \neq 0 in some direction.

Seymour proved that every bridgeless graph has a nowhere zero 6-flow.  Thomas Bloom and I worked this out at the blackboard, and I want to record the ideas here.  First, a folklore observation that a minimal counterexample G must be cubic and 3-connected.  We will temporarily allow graphs to have loops and multiple edges.

We first show that G is 3-edge-connected.  It is certainly 2-edge-connected, else there is a bridge.  (If G is not even connected then look at a component.)  If it were only 2-edge-connected then the graph would look like this.



Contract the top cross edge.


If the new graph has a nowhere zero 6-flow then so will the old one, as the flow in the bottom cross edge tells us how much flow is passing between the left and right blobs at the identified vertices and so the flow the we must put on the edge we contracted.  So G is 3-edge connected.

Next we show that G is 3-regular.  A vertex of degree 1 forces a bridge; a vertex of degree 2 forces the incident edges to have equal flows, so the two edges can be regarded as one.  So suppose there is a vertex v of degree at least 4.


We want to replace two edges xv and yv by a single edge xy to obtain a smaller graph that is no easier to find a flow for.  The problem is that in doing so we might produce a bridge.

The 2-edge-connected components of  G-v are connected by single edges in a forest-like fashion.  If any of the leaves of this forest contains only one neighbour of v then there is a 2-edge-cut, so each leaf contains at least two neighbours of v.


If there is a component of the forest with two leaves then choose x and y to be neighbours of v from different leaves of that component.


Otherwise the 2-edge-connected components of G-v are disconnected from each other.  Now any such component C must contain at least 3 neighbours of v, else there is a 2-edge-cut.  If some C contains 4 neighbours of v then we can choose x and y to be any two of them.  Otherwise all such C contain exactly 3 neighbours of v, in which case there must be at least two of them and we can choose x and y to be neighbours of v in different components.


So G is 3-regular and 3-edge-connected.  If G is only 1-connected then there is no flow between 2-connected components, so one of the components is a smaller graph with no nowhere zero 6-flow.  If G is only 2-connected then because 3 is so small we can also find a 2-edge-cut.

Finally, we want to get rid of any loops and multiple edges we might have introduced.  But loops make literally no interesting contribution to flows and double edges all look like


and the total flow on the pair just has to agree with the edges on either side.

We’ll also need one piece of magic (see postscript).

Theorem. (Tutte) Given any integer flow of G there is a k-flow of G that agrees with the original flow mod k.  (By definition, flows of j in one direction and k-j in the other direction agree mod k.)

So we only need to worry about keeping things non-zero mod k.

The engine of Seymour’s proof is the following observation.

Claim. Suppose that G = G_0 \cup C_1 \cup \cdots \cup C_t where each C_i is a cycle and the number of new edges when we add C_i to G_0 \cup C_1 \cup \cdots \cup C_{i-1} is at most 2.  Then G has a 3-flow which is non-zero outside G_0.  

Write E_i for the set of edges added at the ith stage.  We assign flows to C_t, \ldots, C_1 in that order.  Assign a flow of 1 in an arbitrary direction to C_t; now the edges in E_t have non-zero flow and will never be touched again.  At the next stage, the edges in E_{t-1} might already have some flow; but since |E_{t-1}| \leq 2 there are only two possible values for these flows mod 3.  So there is some choice of flow we can put on C_{t-1} to ensure that the flows on E_{t-1} are non-zero.  Keep going to obtain the desired 3-flow, applying Tutte’s result as required to bring values back in range.

Finally, we claim that the G we are considering have the above form with G_0 being a vertex disjoint union of cycles.  Then G_0 trivially has a 2-flow, and 3 times this 2-flow plus the 2-flow constructed above is a nowhere-zero 6-flow on G.

For F \subseteq G, write [F] for the largest subgraph of G that can be obtained as above by adding cycles in turn, using at most two new edges at each stage.  Let G_0 be a maximal collection of vertex disjoint cycles in G with [G_0] connected, and let H = G - V(G_0).  We claim that V(H) is empty.  If not, then the 2-connected blocks of H are connected in a forest-like fashion; let H_0 be one of the leaves.


By 3-connectedness there are three vertex disjoint paths from H_0 to G_0. At most one of these paths travels through H - H_0; let x and y be endpoints of two paths that do not.  These paths must in fact be single edges, as the only other way to get to G_0 would be to travel through H - H_0.  Finally, since H_0 is 2-connected it contains a cycle through x and y, contradicting the choice of G_0.

Postscript. It turns out that Tutte’s result is far from magical; in fact its proof is exactly what it should be.  Obtain a directed graph H from G by forgetting about the magnitude of flow in each edge (if an edge contains zero flow then delete it).  We claim that every edge is in a directed cycle.


Indeed, choose a directed edge \vec{xy}.  Let Y be the set of vertices that can be reached by a directed path from y and let X be the set of vertices that can reach X by following a directed path.  If \vec{xy} is not in any directed cycle then X and Y are disjoint and there is no directed path from Y to X.  But then there can be no flow in \vec{xy}, contradicting the definition of H.

So as long as there are edges with flow value at least k, find a directed cycle containing one of those edges and push a flow of k through it in the opposite direction.  The total flow in edges with flow at least k strictly decreases, so we eventually obtain a k-flow.