Partitioning infinite matroids into circuits

Although this post is part of the ongoing series on infinite matroids, it begins with an old result about graphs. Suppose that you have a graph $G$, and you want to know whether you can partition it into an edge-disjoint union of cycles. If $G$ is finite then there is a straightforward necessary and sufficient condition for this; every vertex of $G$ should have even degree. Necessity is obvious, and to see sufficiency note that as long as $G$ has any edges it cannot be a tree so it must contain some cycle; after deleting the edges of that cycle, every vertex still has even degree and so we can keep doing the same again until we have exhausted all the edges of the graph. 

What if $G$ is infinite? In that case it is no longer sufficient that all degrees are even. Indeed, consider the two-way infinite ray; every vertex has degree 2, but there isn’t even a single cycle. So we need a stronger condition. Rather than considering individual vertices, we should consider cuts. Since any cycle meets any cut in an even number of edges, if $G$ is an edge-disjoint union of cycles then every cut of $G$ must be a disjoint union of finite sets of even size, so must itself be even or infinite. In 1960, Nash-Williams showed that this condition is sufficient [3].

Theorem 1: Let $G$ be any graph. Then $G$ is an edge-disjoint union of cycles if and only if $G$ has no finite cuts of odd size.

In this post we will examine various generalisations of this result, as well as some of the basic ideas used to prove them, and we will finish up by looking at some intriguing open problems which remain. However, we will not look at Nash-Williams’ original proof, since that was long and technical, and over the years more efficient proofs have emerged (see for example [4],[5]).

Roughly speaking, the modern proofs work in the following way. We want to prove the result by induction on the cardinality of $G$. As the base case, we suppose that $G$ is countable. In this case, we can use an argument like the one above to recursively pick a sequence of edge-disjoint cycles $C_1, C_2,\ldots$ covering all edges of $G$.

For the induction step, we want to break $G$ up into strictly smaller edge-disjoint subgraphs of smaller cardinality, which we will use to cover larger and larger chunks of $G$. We will make sure that each of these subgraphs $H$ has the following properties:

  1. Any finite cut of $H$ is also a cut of $G$
  2. Any finite cut of $G – E(H)$ is also a cut of $G$

The first of these properties is obviously useful, since it ensures in particular that $H$ has no finite odd cuts, so that we can apply the induction hypothesis to it. It is also easy to extend any infinite subgraph $X$ of $G$ to a subgraph $H$ of the same cardinality satisfying (1), by repeatedly taking each finite cut of $X$ which isn’t a cut of $G$ and either adding all edges of the corresponding cut of $G$ (if that cut is finite) or infinitely many edges of the corresponding cut of $G$ (if that cut is infinite). So by closing under this operation we can easily extend any subgraph of $G$ to one satisfying (1).

The second property is used in a recursive construction of our partition of $G$ into subgraphs, to ensure that after we have taken $H$ as a partition class we still have no odd-sized cuts in the remainder and so we can continue finding more partition classes in the same way. Rather surprisingly, there is another operation, akin to that in the previous paragraph but more technical, such that by closing under that operation we can turn any subgraph of $G$ into another of the same cardinality satisfying (2). Even more surprisingly, the exact details of that operation need not be specified for the proof. 

Why not? There is a very useful trick, often applicable in such situations, called the Elementary Submodel Method. It allows us to define a kind of universal closure operation, which when applied to a subgraph closes it under almost anything we might have wanted. That seems miraculous. How could it possibly work? The idea is to make use of a result from logic called the Löwenheim-Skolem Theorem, which states that if we have some theory expressed using first-order logic, some model $M$ of that theory and some infinite subset $X$ of $M$ then we can find a submodel $N$ of $M$ including $X$ and of the same cardinality as $X$, such that any first order statement which holds (in $N$) for some elements of $N$ still holds for those same elements in $M$. $N$ is then called an elementary submodel of $M$. 

A first idea about how to apply this might be to take $M$ to be the graph $G$, considered as a model of the theory of graphs, and define $H$ to be the subgraph corresponding to $N$. Then for any construction we can express in the language of graphs, $H$ will be closed as a subgraph of $G$ under that construction. This is already fairly powerful, but unfortunately the operations under which we want to close subgraphs are often too complicated to be expressible using first-order logic in the language of graphs. So we need a more powerful idea.

What we do instead is to take $M$ to be the whole set-theoretic mathematical universe (considered as a model of the axioms of set theory). Given an infinite subgraph $X$ of $G$ we can now find an elementary submodel $N$ of $M$ including $X$ and containing $G$. If we define $H$ to be the intersection of $N$ with $G$ then it will really be closed under any operation we might think of. Any readers who are logicians might now be worried about whether it is really ok to step outside the set-theoretic universe and treat that whole universe as a mathematical object in this way. Would it then be contained in itself? This is a valid worry, but it can be circumvented by instead taking $M$ to be a large enough fragment of the set-theoretic universe, rather than the whole thing. 

For those reading about the Elementary Submodel Method for the first time, I understand that it can be a little mind-blowing the first time you see it. I remember that it took me a while to wrap my head around it. For the purposes of this post, all you need to remember is that it gives you a kind of universal closure operation, which (with a little more work) lets us push through the proof outlined above.

However, if at some point in life you find yourself working with some recursive construction where you would like to close some infinite graphs (or indeed matroids) under some very technical operations, and perhaps it isn’t easy to see exactly what those operations should be, do consider whether the Elementary Submodel Method might be a helpful tool for you. A good introduction can be found in [4].

What we want to focus on for now is how the same method allows us to extend Nash-Williams’ results to infinite matroids. The obvious question to ask is when we can express the ground set of a matroid as a disjoint union of circuits. In the discussion above, we relied on the fact that every cycle in a graph meets every cut in an even number of edges. The corresponding property in the language of matroids would be that every circuit-cocircuit intersection is even, or to put it another way, that the matroids we are working with are binary. And indeed we do get a corresponding result for infinite binary matroids, with a very similar proof:

Theorem 2: Let $M$ be a finitary binary matroid such that no finite cocircuit of $M$ has odd size. Then the ground set of $M$ is a disjoint union of circuits.

Note that this theorem is only for finitary matroids, which means that all circuits are finite, not for infinite matroids in general. We will return to this point later. However, it turns out that the theorem above can be generalised slightly beyond binary matroids. It turns out that what we need to do is to make precise exactly what it is that is used about circuits in binary matroids for the base case of an argument of the kind outlined above. 

Remember that the base case was that in which $M$ is countable. Our plan there was to recursively define a sequence of disjoint circuits $C_1, C_2, \ldots$ covering the ground set. To define $C_n$, we need that even after we have deleted the circuits $C_1, \ldots, C_{n-1}$ there is still at least one more circuit to choose as $C_n$. To ensure that we ultimately cover the whole ground set, we also need that we can choose this new circuit to contain our favourite edge $e$ of $M$. We can formalise all of this as follows:

Definition: A matroid $M$ is finite matching extendable if for any finite list $C_1, \ldots C_{n-1}$ of disjoint circuits of $M$ and any edge $e$ of $M$ not in any $C_i$ there is some circuit $C_n$ of $M$ containing $e$ and disjoint from all the other $C_i$.

So what we are using to prove the base case in our argument for finitary binary matroids is the fact that they are finite matching extendable. Komjáth, Milner and Polat showed [2] that this property also suffices for the general case.

Theorem 3: Let $M$ be any finitary matroid which is finite matching extendable. Then the ground set of $M$ is a disjoint union of circuits.

Once again, Komjáth, Milner and Polat’s proof was tricky and technical. However, using the Elementary Submodel Method, Attila Joó and I were able to give a much shorter alternative proof. 

Instead of generalising Theorem 1 to matroids, a natural question to ask is what happens for directed graphs. More precisely, a natural question to ask in this context is which directed graphs can be expressed as a disjoint union of directed cycles. After a little thought, it is clear that the following statement is the analog of Theorem 1 for directed graphs:

Theorem 4: Let $D$ be any directed graph such that every for every cut $K$ of $D$ the sets of edges crossing $K$ in each direction have the same cardinality. Then $D$ is a disjoint union of directed cycles. 

The history of this statement is a little peculiar. After proving Theorem 1 in 1960, Nash-Williams claimed that his methods could also be extended to give Theorem 4. However, he did not explain how to do so, and over the decades nobody else was able to reconstruct such a proof. Finally, in 2017 Thomassen gave Theorem 4 as a conjecture in [5], and a few years later this conjecture was finally proved by Attila Joó [1] using the Elementary Submodel Method.

Since we could generalise Theorem 1 to matroids, it is natural to ask whether we can also generalise Theorem 4 to matroids. And indeed we can, namely to regular matroids. To understand the statement, we need to understand what the analog of a directed cycle in a regular matroid might be. A quick way to express the concept we need is by using an alternative characterisation of regular matroids due to White [6].

Definition: A signing of a matroid $M$ consists of a choice, for each circuit $C$ of $M$, of a function $f_C \colon C \to \{-1, 1\}$ and a choice, for each cocircuit $D$ of $M$, of a function $g_D \colon D \to \{-1, 1\}$, such that for any circuit $C$ and cocircuit $D$ we have $$\sum_{e \in C \cap D} f_C(e)g_D(e) = 0\,.$$ A signed matroid is a matroid together with a signing of that matroid.

Fact: A matroid $M$ is regular if and only if it has a signing.

For example we can find a signing of the matroid associated to any graph $G$ by directing the edges of $G$ arbitrarily, defining each $f_C$ to be 1 on the edges directed in one sense around $C$ and -1 for the edges directed in the opposite sense, and defining each $g_D$ to be 1 on the edges crosssing $D$ in one direction and -1 on the edges crossing $D$ in the opposite direction. The desired equation holds since each cycle crosses each cut the same number of times in each direction.

In this context, it is clear what the analog of a directed cycle should be. We say that a circuit $C$ (or cocircuit $D$) of a signed matroid is directed if $f_C$ (or $g_D$) is constant. Attila and I were able to extend Theorem 4 to regular matroids as follows:

Theorem 5: Let $M$ be a finitary signed matroid such that for any cocircuit $D$ of $M$ the sets $g_D^{-1}(-1)$ and $g_D^{-1}(1)$ have the same cardinality. Then the ground set of $M$ is a disjoint union of directed circuits. 

All of the matroidal statements we have considered here were for finitary matroids. The reason for this, however, is not that they are known to be false for infinitary matroids. Indeed, there are no known counterexamples even for general infinite matroids. I would be very interested in any ideas for how to prove or refute the corresponding statements for arbitrary infinite matroids.

However there is an even more basic statement from which the countable case of Theorem 5 quickly follows, which is also open for general infinite matroids. It is an analog of the Farkas Lemma for signed matroids. It would probably be needed for any proof of Theorem 5 for general infinite matroids. So I would be extremely interested in any ideas on how to approach this problem.

Conjecture: Let $M$ be a (possibly infinite) signed matroid and $e$ an edge of $M$. Then $e$ is either contained in a directed circuit or a directed cocircuit. 

References:

[1] Attila Joó. “On partitioning the edges of an infinite digraph into directed cycles”. In: Advances in Combinatorics 2.8 (2021)
[2] Péter Komjáth, Eric Charles Milner, and Norbert Polat. “A compactness theorem for perfect matchings in matroids”. In: Journal of Combinatorial Theory, Series B 44.3 (1988), pp. 253–262.
[3] C. St. J. A. Nash-Williams. “Decomposition of graphs into closed and endless chains”. In: Proceedings of the London Mathematical Society 3.1 (1960), pp. 221–238.
[4] Lajos Soukup. “Elementary submodels in infinite combinatorics”. In: Discrete Math- ematics 311.15 (2011), pp. 1585–1598.
[5] Carsten Thomassen. “Nash-Williams’ cycle-decomposition theorem”. In: Combina- torica 37.5 (2017), pp. 1027–1037.
[6] Neil White. Matroid applications. 40. Cambridge University Press, 1992.

Sparse representations of binary matroids

Let us call the sparsity of a binary matroid $M$ the minimum number of $1$’s in a binary matrix representation of $M$. There are many practical reasons to desire such a representation – matrices with few non-zero entries can be encoded and manipulated much more efficiently. So it would be really nice to have an algorithm which can efficiently perform row reductions in order to sparsify a binary matrix, even to within a very rough approximation of the optimal value.

Given the importance of this problem, I’m surprised that I haven’t seen more work on it from the matroid theory community. So this blog post aims to publicize the area, share some questions I have been wondering about, and ask the community for pointers to more work in this direction.

We consider classes of binary matroids which are closed under minors. For which such classes is the sparsity at most linear in the rank? Parallel edges cause silly problems, so we only consider the simple matroids in the class. If the number of elements is not linear in the rank, then neither is the sparsity. So a necessary condition is that the class forbids the cycle matroid of some clique. Is this obvious necessary condition also sufficient? Surely not, right?!

Problem 1: Is it true that for each integer $t$, there exists an integer $c=c(t)$ so that if $M$ is a simple rank-$r$ binary matroid without a minor isomorphic to the cycle matroid of a $t$-vertex clique, then $M$ has a representation with at most $c r$ many $1$’s?

Such a matroid $M$ has at most linearly many elements by the Growth Rate Theorem. Problem 1 asks if we can additionally bound the average number of $1$’s per column. This is possible for graphic classes since we can just take the incidence matrix. However, I would guess that the answer is no in general, even for matroids of bounded branch-width.

What if we only consider representations which are in reduced row echelon form? This added restriction probably makes it much harder to sparsify. For a graph $G$ from a minor-closed class, this means that we want to bound the minimum, over all spanning trees $T$, of the average length of a fundamental cycle. (For each edge $e \in E(G) \setminus E(T)$ , its fundamental cycle is the unique cycle of $T+e$.) The best candidate I have found for a graph that is not sparsifiable in this sense is a big grid.

Problem 2: Is it true that for each integer $k$, there exists an integer $n=n(k)$ so that if $T$ is any spanning tree of the $n \times n$ grid, then the average length of a fundamental cycle with respect to $T$ is at least $k$?

Pablo Blanco’s undergraduate thesis [1] at Princeton considered another notion of sparsity which is typically harder to obtain. From the matrix perspective, we want to find a matrix which is in reduced row echelon form and, additionally, avoids an all $1$’s submatrix of fixed size. He proposed a conjecture about graphs which suggests that the main obstructions are the following (planar) graph and its dual. The $k$-banana is the graph which is obtained from $k$ paths of length $k$ by identifying all of their starting vertices into one vertex $s$, and all of their ending vertices into one vertex $t$.

Let me also point out that Kristýna Pekárková has a really nice master’s thesis [2] which takes care of the bounded branch-depth case of sparsification quite nicely.

[1] Pablo Blanco, Graphs with Large Overlap in Their Spanning Trees, click here for info

[2] Kristýna Pekárková, Matroid Based Approach to Matrix Sparsification, click here for pdf

Delta-Modular Matroids

The goal of this post is to entice readers to work with an interesting class of matroids that has important connections with the theory of integer programming. These matroids are called Delta-modular, and they are a natural generalization of regular matroids. After giving some background information, I will state and discuss some fundamental properties of Delta-modular matroids, and then state and discuss some important open problems concerning these matroids. 

Introduction

A fundamental problem in integer programming is to solve the following Integer Linear Program (ILP): find $\textrm{max}\{c^Tx \mid Ax \le b \textrm{ and } x \in \mathbb Z^n\}$ where $A \in \mathbb Z^{m \times n}$, $b \in \mathbb Z^m$, and $c \in \mathbb Z^n$. This problem is $\mathcal N\mathcal P$-hard, but we can hope for better efficiency if the matrix $A$ has special structure. Given a positive integer $\Delta$, an integer matrix $A$ is $\Delta$-modular if the absolute value of every $\textrm{rank}(A) \times \textrm{rank}(A)$ submatrix of $A$ is at most $\Delta$. A classic result of Hoffman and Kruskal [HK56] implies that ILPs over 1-modular (or unimodular) matrices can be solved in strongly polynomial time. After about 60 years with little progress, a breakthrough result of Artmann, Weismantel, and Zenklusen showed that ILPs over 2-modular (or bimodular) matrices can be solved in strongly polynomial time [AWZ17]. Determining the complexity for solving ILPs over $\Delta$-modular matrices with $\Delta \ge 3$ is a major open problem in the theory integer programming.

The proof of Artmann, Weismantel, and Zenklusen heavily relies on structural properties of 2-modular matrices. This is where matroids come in! For each positive integer $\Delta$ we say that a matroid is $\Delta$-modular if it has a representation over $\mathbb Q$ as a $\Delta$-modular matrix, and we write $\mathcal M_{\Delta}$ for the class of $\Delta$-modular matroids. The hope is that studying $\mathcal M_{\Delta}$ via techniques from structural matroid theory will uncover new properties of $\Delta$-modular matrices that are relevant for integer programming. However, $\mathcal M_{\Delta}$ is also a very natural class of matroids, because $\mathcal M_1$ is precisely the class of regular matroids!

Properties

Even though $\mathcal M_{1}$ is fundamental in integer programming and matroid theory, little is known about $\Delta$-modular matroids when $\Delta \ge 2$. Below is a list of five properties that will likely be important for future work with these matroids.

Property 1: For all $\Delta \ge 1$, the class $\mathcal M_{\Delta}$ is closed under minors and duality.

Closure under minors was proved in [Proposition 8.6.1, GNW22] and closure under duality was proved by D’Adderio and Moci [Theorem 2.2, DM13] using arithmetic matroids, although a more direct proof due to Marcel Celaya can be found in [Theorem 4.2, OW22]. Of course, Property 1 is crucial for using techniques from structural matroid theory to study $\mathcal M_{\Delta}$.

Property 2: For all $\Delta \ge 1$, every matroid in $\mathcal M_{\Delta}$ is representable over every field with characteristic greater than $\Delta$.

This follows from the fact that if $A$ is a $\Delta$-modular matrix and $p$ is a prime greater than $\Delta$, then the set of all $\textrm{rank}(A)\times \textrm{rank}(A)$ submatrices of $A$ with non-zero determinant does not change if we perform all calculations modulo $p$. In particular, every matroid in $\mathcal M_2$ is dyadic, and every matroid in $\mathcal M_3$ is $\textrm{GF}(5)$-representable. Also, since there is a prime in the interval $(\Delta, 2\Delta]$ by Chebyshev’s Theorem, Property 2 implies that the uniform matroid $U_{2, 2\Delta + 2}$ is not in $\mathcal M_{\Delta}$. Again, Property 2 is crucial because it allows for the potential use of tools from the study of matroids representable over partial fields.

Property 3: When $\Delta \ge 2$, the class $\mathcal M_{\Delta}$ is not closed under direct sums.

This is where $\mathcal M_{\Delta}$ differs from matroid classes defined by representability over a partial field, which are always closed under direct sums. Intuitively, Property 3 is true because a block-diagonal matrix for which each block is $\Delta$-modular may not be $\Delta$-modular itself. As a particular example of Property 3, the uniform matroid $U_{2,4}$ is in $\mathcal M_2$, but $U_{2,4} \oplus U_{2,4}$ is not in $\mathcal M_2$ [Proposition 4.4, OW22].  

Property 4: If $r$ is sufficiently large as a function of $\Delta$, then every simple rank-$r$ matroid in $\mathcal M_{\Delta}$ has at most $\binom{r+1}{2} + 80\Delta^7 \cdot r$ elements [Theorem 1.3, PSWX24].

The problem of finding upper bounds on the number of columns of a rank-$r$ $\Delta$-modular matrix has received a lot of recent attention, because this also provides bounds for important parameters associated with ILPs over $\Delta$-modular matrices; see [LPSX23] for details. The bound in Property 4 was proved with techniques from matroid theory, and is currently the best known bound for fixed $\Delta$.

Property 5: $\mathcal M_{\Delta}$ contains no spikes with rank greater than $2\Delta$ [Proposition 2.1, PSWX24].

This was a key fact used in the proof of Property 4, and also differs from the typical situation for matroids over partial fields. Since spikes are notoriously `wild’, this is good news!

Open Problems

Since the study of $\Delta$-modular matroids is so new, there are many open problems.

Problem 1: Let $\mathcal M_{\Delta}^t$ be the class of matroids with a representation as a totally $\Delta$-modular matrix. Is $\mathcal M_{\Delta}^t = \mathcal M_{\Delta}$?

An integer matrix $A$ is totally $\Delta$-modular if the determinant of every square submatrix has absolute value at most $\Delta$. When $\Delta \le 2$, every $\Delta$-modular matrix is projectively equivalent to a totally $\Delta$-modular matrix, so Problem 1 has an affirmative answer when $\Delta \le 2$. However, when $\Delta \ge 3$ it is unclear whether every $\Delta$-modular matrix is projectively equivalent to a totally $\Delta$-modular matrix, so new ideas may be needed.

Problem 2: Find an absolute constant $C$ so that if $r$ is sufficiently large, then every simple rank-$r$ matroid in $\mathcal M_{\Delta}$ has at most $\binom{r+1}{2} + C\Delta\cdot r$ elements.

This is a natural next step from the bound in Property 4. There is a lower bound of $\binom{r+1}{2} + (\Delta – 1)(r – 1)$ that holds for all $\Delta \ge 1$ (see [Proposition 1, LPSX23]), so the bound in Problem 2 would be tight up to the constant $C$. While this lower bound is the correct answer for $\Delta = 1$ [Hel57] and $\Delta = 2$ [OW22, LPSX23], a recent construction of Averkov and Schymura [Theorem 1.3, AS22] does better for $\Delta \in \{4, 8, 16\}$, so there is no obvious choice for $C$ that is tight for all $\Delta$. 

Problem 3: Find a class $\mathcal N$ of matroids so that ILPs with $\Delta$-modular constraint matrix $A$ with $M(A) \in \mathcal N$ can be solved in polynomial time.

This is in the spirit of a recent result showing that ILPs over $\Delta$-modular matrices for which each row has at most two nonzero entries can be solved in strongly polynomial time [FJWY22]. Perhaps the most interesting choice for $\mathcal N$ would be the class of projections of graphic matroids, because the matroids in $\mathcal M_{\Delta}$ providing the best known lower bound in Problem 2 are projections of graphic matroids.

Problem 4: Find the list of excluded minors for $\mathcal M_{2}$.

Since $\mathcal{ M}_{\Delta}$ is minor-closed and every matroid in $\mathcal M_{\Delta}$ is representable over finite fields by Property 2, Rota’s Conjecture says that $\mathcal M_{\Delta}$ has a finite list of excluded minors. Tutte showed that $\mathcal M_{1}$ has only three excluded minors: $U_{2,4}$, the Fano plane $F_7$, and its dual $F_7^*$ [Tut58]. It should be feasible (but difficult) to use existing techniques to find the list of excluded minors when $\Delta = 2$; there is a more detailed discussion in [OW22]. 

Problem 5: Find a decomposition theorem for $\mathcal M_{2}$.

Seymour’s celebrated decomposition theorem says that every internally $4$-connected matroid in $\mathcal M_1$ is graphic, cographic, or a specific $10$-element matroid $R_{10}$ [Sey80]. Do matroids in $\mathcal M_2$ satisfy a similar property? If a decomposition can be found efficiently for $\mathcal M_2$, it would have several interesting consequences. For example, it would likely give a new proof that ILPs over 2-modular matrices can be solved in polynomial time. It would also likely give a polynomial-time algorithm for determining if a given matrix is 2-modular; this recognition problem is open for $\Delta$-modular matrices when $\Delta \ge 2$. While Problem 5 is certain to be difficult, it would likely be worthwhile to use the approach of [MNvZ11] and [CMN15] to systematically study what such a decomposition would look like. Of course, this problem is also highly interesting for $\mathcal M_{\Delta}$ with $\Delta \ge 3$.

References

[AWZ17] S. Artmann, R. Weismantel, R. Zenklusen. A strongly polynomial algorithm for bimodular integer linear programming. In Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, pages 1206-1919, 2017.

[AS22] G. Averkov, M. Schymura. On the maximal number of columns of a Delta-modular matrix. In K. Aardal and Laura L. Sanità, editors, Integer Programming and Combinatorial Optimization, pages 29-42, Cham, 2022. Springer International Publishing.

[CMN15] C. Chun, D. Mayhew, M. Newman. Obstacles to decomposition theorems for sixth-root-of-unity matroids. Ann. Combin., 19:79-93, 2015.

[DM13] M. D’Adderio, L. Moci. Arithmetic matroids, the Tutte polynomial, and toric arrangements. Adv. in Math., 232:335-367, 2013.

[FJWY22] S. Fiorini, G. Joret, S. Weltge, Y. Yuditsky. Integer programs with bounded subdeterminants and two nonzeros per row. In 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS), pages 13-24, 2022.

[GNW22] J. Geelen, P. Nelson, Z. Walsh. Excluding a line from complex-representable matroids. To appear in Mem. Amer. Math. Soc.

[Hel57] I. Heller. On linear systems with integral valued solutions. Pac. J. Math., 7(3):1351-1364, 1957.

[HK56] A. Hoffman, J. Kruskal. Integral boundary points of convex polyhedra. Linear Inequalities and Related Systems, 24:223-246, 1956.

[LPSX23] J. Lee, J. Paat, I. Stallknecht, L. Xu. Polynomial upper bounds on the number of differing columns of Delta-modular integer programs. Math. Oper. Res., 48(4):1811-2382, 2023.

[MWvZ11] D. Mayhew, G. Whittle, S. H. M. van Zwam. An obstacle to a decomposition theorem for near-regular matroids. SIAM J. Disc. Math., 25(1):271-279, 2011.

[OW22] J. Oxley, Z. Walsh. 2-Modular matrices. SIAM J. Disc. Math., 36:1231-1248, 2022.

[PSWX24] J. Paat, I. Stallknecht, Z. Walsh, L. Xu. On the column number and forbidden submatrices for Delta-modular matrices. SIAM J. Disc. Math., 38:1-18, 2024.

[Sey80] P. D. Seymour. Decomposition of regular matroids. J. Combin. Theory Ser. B, 28:305-359, 1980.

[Tut58] W. T. Tutte. A homotopy theorem for matroids, I, II. Trans. Amer. Math. Soc., 88, 144-174, 1958.

 

 

Online Talk: James Oxley

YouTube recording: https://www.youtube.com/watch?v=fKgb4XxhQzk

Time: Wednesday, April 24 at 3pm ET
Zoom: https://gatech.zoom.us/j/8802082683

Speaker: James Oxley, Louisiana State University
Title: The legacy of Dominic Welsh

Abstract: Dominic Welsh wrote the first comprehensive text on matroids. He supervised 28 doctoral theses and wrote over 100 papers and five books. As impressive as these numbers are, they fail to truly capture the spirit of the man who inspired generations of students and imbued them with not only a love of mathematical beauty but with a deep and abiding affection for the man himself. Dominic died in November, 2023. The speaker will attempt to capture some of the key aspects of his legacy.