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Description
We consider programmable matter as a collection of simple computational elements (or particles) that self-organize to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, in which the particle system gathers as tightly together as possible, as in a sphere or its equivalent in

We consider programmable matter as a collection of simple computational elements (or particles) that self-organize to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, in which the particle system gathers as tightly together as possible, as in a sphere or its equivalent in the presence of some underlying geometry. Within this model a configuration of particles can be represented as a unique closed self-avoiding walk on the triangular lattice. In this paper we will examine the bias parameter of a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, for particle systems that begin in a connected configuration with no holes. This bias parameter, $\lambda$, determines the behavior of the algorithm. It has been shown that for $\lambda > 2+\sqrt{2}$, with all but exponentially small probability, the algorithm achieves compression. Additionally the same algorithm can be used for expansion for small values of $\lambda$; in particular, for all $0 < \lambda < \sqrt{\tau}$, where $\lim_{n\to\infty} {(p_n)^{1
}}=\tau$. This research will focus on improving approximations on the lower bound of $\tau$. Toward this end we will examine algorithmic enumeration, and series analysis for self-avoiding polygons.
ContributorsLough, Kevin James (Author) / Richa, Andrea (Thesis director) / Fishel, Susanna (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description
Van der Waerden’s Theorem asserts that for any two positive integers k and r, one may find an integer w=w(k,r) known as the Van der Waerden Number such that for every r-coloring of the integers from 1 to w there exists a monochromatic arithmetic progression of length k. This groundbreaking

Van der Waerden’s Theorem asserts that for any two positive integers k and r, one may find an integer w=w(k,r) known as the Van der Waerden Number such that for every r-coloring of the integers from 1 to w there exists a monochromatic arithmetic progression of length k. This groundbreaking theorem in combinatorics has greatly impacted the field of discrete math for decades. However, it is quite difficult to find the exact values of w. As such, it would be worth more of our time to try and bound such a value, both from below and above, in order to restrict the possible values of the Van der Waerden Numbers. In this thesis we will endeavor to bound such a number; in addition to proving Van der Waerden’s Theorem, we will discuss the unique functions that bound the Van der Waerden Numbers.
Created2019-12
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Description
The Super Catalan numbers are a known set of numbers which have so far eluded a combinatorial interpretation. Several weighted interpretations have appeared since their discovery, one of which was discovered by William Kuszmaul in 2017. In this paper, we connect the weighted Super Catalan structure created previously by Kuszmaul

The Super Catalan numbers are a known set of numbers which have so far eluded a combinatorial interpretation. Several weighted interpretations have appeared since their discovery, one of which was discovered by William Kuszmaul in 2017. In this paper, we connect the weighted Super Catalan structure created previously by Kuszmaul and a natural $q$-analogue of the Super Catalan numbers. We do this by creating a statistic $\sigma$ for which the $q$ Super Catalan numbers, $S_q(m,n)=\sum_X (-1)^{\mu(X)} q^{\sigma(X)}$. In doing so, we take a step towards finding a strict combinatorial interpretation for the Super Catalan numbers.
ContributorsHouse, John Douglas (Author) / Fishel, Susanna (Thesis director) / Childress, Nancy (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05