Calendar

Posted October 31, 2026

Informal Combinatorics Seminar

Joy Harris, Louisiana State University
Equitable Graph Colorings

Say that we are running a factory and we need to assign workers to tasks within that factory. In order to avoid cross contamination, we specify that certain pairs of tasks should not be completed by the same worker. We can think of the tasks as the vertices of a graph, and pairs of tasks that are incompatible will have an edge between them. Now color each vertex based on which worker we have assigned to the task. Ideally, we want that none of the tasks assigned to the same worker are incompatible, i.e. none of the vertices of the same color have an edge between them. This is exactly the idea behind a proper vertex coloring of a graph. Let's add an extra condition. It's not fair to one worker if they must work twice as many tasks as their peers. We specify that each worker must be assigned to the same amount of tasks, give or take one. In the graph, this means that each vertex color class must be the same size, give or take one vertex. We have now entered the world of equitable vertex graph colorings. We will see how equitable graph vertex colorings differ from vertex colorings, discuss the Chen-Lih-Wu conjecture and what's known, and then prove some basic results that give the flavor of the methods used in the proof of the conjecture.

Event contact: jgarc86@lsu.edu

Posted August 26, 2026

Informal Analysis Seminar Questions or comments?

Jackson Knox, Louisiana State University
TBD (Dynamical Systems)

Event contact: Laura Kurtz

Posted September 25, 2026
Last modified October 17, 2026

Geometry and Topology Seminar Seminar website

Steven Clontz, University of South Alabama
Database-Driven Mathematical Inquiry in Topology

Recent advances in computing have changed not only the nature of mathematical computation, but mathematical proof and inquiry itself. While artificial intelligence and formalized mathematics have been the major topics of this conversation, this talk will explore another class of tools for advancing mathematics research: databases of mathematical objects that enable semantic search, particularly the π-Base community database of topological counterexamples

Posted August 27, 2026

Informal Geometry and Topology Seminar Questions or comments?

Michael Young, Louisiana State University
TBD

TBD

Posted October 16, 2026

Student Colloquium

Steven Clontz, University of South Alabama
Accessible and Interactive Mathematical Documents for Instruction

Beginning April 26, 2027, all public universities will be required to ensure digital documents posted online must be accessible to screenreader users. Compliance with the Americans with Disabilities Act can be quite tricky in mathematics, given our reliance on intricate formulas and graphics, and even interactive elements such as code or three-dimensional renders, for research and education. In this talk we will explore three particular tools for meeting this upcoming accessibility requirement: PreTeXt, Prefigure, and Doenet.

Posted September 30, 2026

Control and Optimization Seminar Questions or comments?

Daniel Wachsmuth, University of Wuerzburg, Germany
Solution Algorithms for Nonsmooth Optimal Control Problems Based on the Pontryagin Maximum Principle

The talk is motivated by optimal control problems for partial differential equations. The main interest is in problems that involve integral functions of the type \[\int_\Omega g(u(x)) \ dx,\] where $g : \mathbb R \to \mathbb R$ is nonsmooth and nonconvex, and $u$ is the control variable. Examples involve $L^{\scriptscriptstyle r}$-pseudonorms with $r\in [0,1)$ or problems with the constraint $u(x) \in \mathbb Z$ for $x\in \Omega$. Due to the nonconvexity of $g$, the existence of solutions in $L^{\scriptscriptstyle p}$-spaces cannot be proven. Local solutions (if they exist) can be proven to satisfy the Pontryagin maximum principle. A distinctive feature of this result is that no differentiability with respect to the control variable $u$ is required. The maximum principle serves as a starting point to develop an implementable solution algorithm. We prove that the residual in the maximum principle vanishes along the iterates produced by the algorithm. Under additional assumptions, the iterates are a minimizing sequence.