Sorting Index and Mahonian-Stirling pairs for labeled forests
Björner and Wachs defined a major index for labeled plane forests and showed that it has the same distribution as the number of inversions. We will define and study the distributions of a few other natural statistics on labeled forests. Specifically, we introduce the notions of bottom-to-top maxima, cyclic bottom-to-top maxima, sorting index and cycle minima. Then we show that the pairs (inv, Btmax), (sor, Cyc), and (maj, Cbtmax) are equidistributed. Our results extend the result of Bj¨orner and Wachs and generalize results for permutations.
Properties of the Promotion Markov Chain on Linear Extensions
The Tsetlin library is a model for the way an arrangement of books on a library shelf evolves over time. It assumes that, given n books, one book is read and returned at the end of the shelf before another one is picked up. Suppose the probability that a book i is picked up is x_i. An interesting property of this Markov chain is that its eigenvalues can be computed exactly and they are linear in the x_i ‘s. This result has been generalized in various ways by various people. In this work, we investigate the extended promotion Markov Chain introduced by Ayyer, Klee, and Schilling in 2014. They showed that for a poset that is a rooted forest, the transition matrix has eigenvalues that are linear in x_1,…, x_n. We show the same result for a larger class of posets.
Ideal class (semi)groups and factorization in Prüfer domains
The ideal class group helps explain factorization properties in Dedekind domain. Can we use the ideal class group to gain information about factorization in generalizations of Dedekind domain? After exploring this question, we will turn our attention to the task of computing an ideal class semigroup. We will then discuss what information about factorization can be obtained from the ideal class semigroup.
Refreshments at 3:00 in the Martin-O foyer
Lattice ideals and coding theory
By issues of the destiny, in the world of mathematics there are two (at least) different objects called “lattices”. One of them is related with the concept of an ordered set and we will not talk about this one. Another object which is also called a lattice is just defined as a subgroup of Z^n. Given a lattice L, we associate a binomial ideal I(L) called “lattice ideal”.
We will study some of the main properties of lattice ideals, for instance given a set of generators of L, how to find I(L), and given a set of generators of I(L), how to find L. We will see also how to identify if an arbitrary binomial ideal comes from a lattice, and if this is the case, how to find such a lattice.
Finally, we will see how we can apply lattice ideals to coding theory.
Ideal Graphs and Adjacency Conditions
Jim Coykendall, Clemson
Recently there has been much activity in the intersection of commutative algebra and graph theory. In particular, there has been much attention to the so called “zero-divisor graph” and a number of its natural variants. The idea behind the zero-divisor graph is to take as vertices the nonzero zero-divisors of a commutative ring with identity and declare that there is an edge between $x,y\in R$ if and only if $xy = 0$. There are a number of striking properties that this graph possesses, probably the most interesting of which is that this graph is connected and has diameter no more than 3.
In this talk we expand this idea to a larger arena. In particular, we fix a commutative ring and define a graph by letting the vertex set be (a subset of) the set of ideals. Under various defining conditions for edges, we determine properties of the graph that lend insights to the algebraic structures involved.
On the Perfect Reconstruction of the Structure of Dynamic Networks
The network inference problem consists in reconstructing the structure or wiring diagram of a dynamic network from time-series data. Even though this problem has been studied in the past, there is no algorithm that guarantees perfect reconstruction of the structure of a dynamic network. In this talk I will present a framework and algorithm to solve the network inference problem for discrete-time networks that, given enough data, is guaranteed to reconstruct the structure with zero errors. The framework uses tools from algebraic geometry.