# Wednesday January 11th

## Pre-Notes

• Be familiar with flats, parallel and series connections. They also refer to bicircular matroids from chapter 6.
• Hall’s Marriage theorem is discussed (recap from Graph Theory), although they do go over the definition is kind of a hilarious way (some example about a primitive town) in the context of sub modular functions and family sets of a traversal.
• Delta-wye exchange finally covered!
• I found a neat Isabelle proof of Hall’s Marriage Theorem by Jiang D. And Nipkow T (2022) link with code here.
• Having understood the problem, I’m very interested to see how they did this in Isabelle (I’ve done very simple proofs in the past in Lean, Agda and Coq so it’s not totally new, but I haven’t played around with Isabelle much. There is a friend of mine from years ago who live-s/ed in Portland who was SUPER into Haskell and Lean! He contacted me a while ago about working at a company but I had to remind him I’m in grad school lol).
• What’s cool is that they covered Simplex. I found a neat resource that covers some of this, and Scarf’s proof of Brouwer’s fixed point theorem. I will look at both resources tomorrow during the day.
• My notes are kind of messy today, and most are in my book, because I read the chapter earlier, and when I started writing them up, I had to stop for Quantum Computing book club! However (and this is really cool!) we’re having an off-week session (we meet every two weeks) to work through the exercises. I invited a friend who is awesome and has the perfect background for this stuff, and it was a LOT of fun hanging out, and the group is super nice!!! What’s completely on me is that I kind of have to get up early in the morning, so these are not the optimal notes :)
• I do want to check out the lattice path matroid papers. It’s already super late here, so that’s probably going to be a tomorrow thing.
• For a set E, a function f from 2^{E} into R is sub modular if f(X) + f(Y) \geq f(X \cup Y) + f(X \cap Y) for all subsets X and Y of E.
• Let M(f) denote a matroid on E s.t. C(f) is its set of circuits. We say M(f) is induced by f. A subset I of E is independent in M(f) iff | I’ | \leq f(I’) for all non empty sets I’ of I.
• A connected graph H has at least two cycles iff |V(H)| \leq |E(H)| - 1.
• \Theta graphs, loose handcuffs and tight handcuffs. (See Fig 11.1)
• We consider increasing sub modular functions s.t. f(\null) = 0. We can specify the rank function M(f) of f. We call this function f a polymatroid on 2^E. Other names for these functions are (1) polymatroid functions (2) integer polymatroids (3) integer polymatroid functions.
• Proof 11.1.9: Gist is that freely adding a point on an element of a polymatroid is the same as that of freely adding a point on the flat of a matroid. If \varphi is a function from E into the set of flats of M: f(X) = r_M(\bigcup_{x \in X}\varphi(x))
• Let \epsilon be a collection of subsets of a set E s.t. \epsilon is a lattice under set inclusion. In such a case, we call \epsilon a lattice of subsets of E. A function t from \epsilon into R is sub modular on \epsilon if t(X) + t(Y) \geq t(X \vee Y) + t(X \wedge Y) for all X and Y in \epsilon.
• The theorems of Hall and Radio: Let A be a family (A_i : j \in J) of subsets of a set S. Under what circumstances does M[A] have a transversal? (Marriage Problem due to Rado (1942)).
• Hall’s Theorem: A family (A_j : j \in J) of subsets of a set S has a transversal iff for all K \subset J, |A(K)| \geq |K|.
• Rado’s Theorem: Let (A_j : j \in J) be a family of subsets of a set S and let M be a matroid on S having rank function r. Then (A_j : j \in J) has a transversal that is independent in M iff for all K \subset J, r(A(K)) \geq | K |.
• Difference between transversals and a system of representatives is that the latter need not be distinct.
• Partial transversals: Let A be a family (A_j : j \in J) of subsets of a set S and let M be a matroid on S having rank function r. Let d be a non-negative integer not exceeding | J |. Then A has a partial transversal of size |J | - d that is independent in M iff for all subsets K of J, r(A(K)) \geq | K | - d.
• A transversal matroid is algebraic over all fields. Proof of this : The surjection \sigma is simple if | J | = | S | + 1. As every surjection is the composition of at most | J | - 1 simple surjections, it suffices to prove the proposition in the case that \sigma is simple.
• L(G, M) is the set of independent sets of a matroid on V. We obtain each non-loop edge of G by two oppositely directed edges, deleting each loop from G, and then finding the matroid induced from M by the resulting directed graph.
• A matroid of the form L(\Delta, M_{+}) where M is free is called a principal transversal matroid or a fundamental transversal matroid.
• Simplex: If | J | = m, a free matroid on J can be represented geometrically by m affinity independent points in R^{m - 1}. In general, such a collection of affinity independent points is called a simplex. A vertex of the simplex is any of the points of J; a face of the simplex is any flat of the affine matroid on J.
• Transversal matroids that are also cotransversal are sometimes called bitransversal.
• Lattice path matroids are interesting: I’d like to read more about them!
• The class of lattice path matroids is closed under taking duals.
• Nested matroids (Crapo 1965) : generalised Catalan matroids by Bonnie, de Pier and Noy (2003).
• For a positive integer n, the rank n Catalan matroid is the transversal matroid with ground set {1, 2, …, 2n} having the family (1, 2i - 1] : 1 \leq i \leq n) of intervals as a presentation. Its name derives from the fact that its number of bases is the Catalan number \frac{1}{n + 1} (2n \choose n)
• Bonin, de Mier and Nou (2003) proved that nested matroids are precisely the matroids that can be obtained from the empty matroid by iterating the operations of adding a coloop and taking a free extension.
• Matroid union: Let \phi_1 and \phi_2 be bijections from E onto disjoint sets E_1 and E_2. Then each \phi_i induces an isomorphic copy of M_i on E_i. Hence on E_1 \cup E_2, we have a matroid isomorphic to M_1 \oplus M_2. The matroid M_1 \vee M_2 is called the union of M_1 and M_2. A matroid is transversal iff it is a union of rank-1 matroids. A series connection of transversal matroids.
• Let M_1 and M_2 be matroids.
• If E(M_1) \cap E(M_2) = \null, then M_1 \vee M_2 = M_1 \oplus M_2 (direct sum)
• If E(M_1) \cap E(M_2) = {p} then M_1 \vee M_2 = S(M_1, M_2) (series connection)
• M is reducible if it can be written as the union of two matroids neither of which is equal to M; otherwise M is irreducible.
• Matroid partitioning algorithm : polynomial time. For a fixed k and a given matroid M, our algorithm will either produce a partition of E(M) into k independent sets or establish that no such partition exists
• Matroid intersection algorithm
• Amalgams, free amalgams (see 11.4) If the function \zeta is submodular, then it is the rank function of a matroid on E. Moreover, this matroid is the free amalgam of M_1 and M_2.
• Fully embedded: let M be a matroid and suppose that Z \subset Z’ \subset E(M). Assume that every element of Z’ is fully embedded in M iff Z is fully embedded in M. If T = E_1 \cap E_2, if T is fully embedded in M_1, then \eta is sub modular on L(M_1, M_2) and hence the proper amalgam of M_1 and M_2 exists.
• Generalised parallel connection
• Delta-wye exchange

## Other

• Went to the Quantum group today. We are on chapter 2! We were talking about Tensor Products today and math3ma has a great blog post on tensor products! link.
• We also spoke about Quantum Mechanics and L2 norms and the complex conjugate that behaves like a norm of the function. We decided upon working on the exercises that our off-weeks should be used for study on our Discord. We’re working out document-sharing (and collaboratively working) with the group for our formal meetings, because with all this tensor operation stuff and bra ket notation, we kind of need to draw matrices and with all of our backgrounds talking about if this is the same as dot product or if this notation is the same as inverse or transpose, we need a common space.
• What was really wild about the first time I read chapter 2 on my own was how much it reminded me of Spectral class! When they talk about matrices, I imagine them as graphs (say G_1 and G_2) and then I can think about their tensor products and that sort of thing. There is even spectral decomposition. But who knows how long I can use this analogy. I can totally see how Quantum Graph Theory is a thing, though!
• I’m also not going to lie; this is the first chapter I thought you know, this could be a thing I could write in say, Haskell, and describe operations over, as the types are Vectors. And then I could check if my answers are correct based on the operations. Because the operations seem very mappable. So like, for different types of mappings, the constraint would be something like, the matrix has to be the same size, or whatever. And this mapping could be a type of product, e.g. Kronecker or whatever. And one could easily check things like congruency, or isHermitian or something.