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This dissertation will cover two topics. For the first, let $K$ be a number field. A $K$-derived polynomial $f(x) \in K[x]$ is a polynomial that

factors into linear factors over $K$, as do all of its derivatives. Such a polynomial

is said to be {\it proper} if

its roots are distinct. An

This dissertation will cover two topics. For the first, let $K$ be a number field. A $K$-derived polynomial $f(x) \in K[x]$ is a polynomial that

factors into linear factors over $K$, as do all of its derivatives. Such a polynomial

is said to be {\it proper} if

its roots are distinct. An unresolved question in the literature is

whether or not there exists a proper $\Q$-derived polynomial of degree 4. Some examples

are known of proper $K$-derived quartics for a quadratic number field $K$, although other

than $\Q(\sqrt{3})$, these fields have quite large discriminant. (The second known field

is $\Q(\sqrt{3441})$.) I will describe a search for quadratic fields $K$

over which there exist proper $K$-derived quartics. The search finds examples for

$K=\Q(\sqrt{D})$ with $D=...,-95,-41,-19,21,31,89,...$.\\

For the second topic, by Krasner's lemma there exist a finite number of degree $n$ extensions of $\Q_p$. Jones and Roberts have developed a database recording invariants of $p$-adic extensions for low degree $n$. I will contribute data to this database by computing the Galois slope content, inertia subgroup, and Galois mean slope for a variety of wildly ramified extensions of composite degree using the idea of \emph{global splitting models}.
ContributorsCarrillo, Benjamin (Author) / Jones, John (Thesis advisor) / Bremner, Andrew (Thesis advisor) / Childress, Nancy (Committee member) / Fishel, Susanna (Committee member) / Kaliszewski, Steven (Committee member) / Arizona State University (Publisher)
Created2019
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Description
I focus on algorithms that generate good sampling points for function approximation. In 1D, it is well known that polynomial interpolation using equispaced points is unstable. On the other hand, using Chebyshev nodes provides both stable and highly accurate points for polynomial interpolation. In higher dimensional complex regions, optimal sampling

I focus on algorithms that generate good sampling points for function approximation. In 1D, it is well known that polynomial interpolation using equispaced points is unstable. On the other hand, using Chebyshev nodes provides both stable and highly accurate points for polynomial interpolation. In higher dimensional complex regions, optimal sampling points are not known explicitly. This work presents robust algorithms that find good sampling points in complex regions for polynomial interpolation, least-squares, and radial basis function (RBF) methods. The quality of these nodes is measured using the Lebesgue constant. I will also consider optimal sampling for constrained optimization, used to solve PDEs, where boundary conditions must be imposed. Furthermore, I extend the scope of the problem to include finding near-optimal sampling points for high-order finite difference methods. These high-order finite difference methods can be implemented using either piecewise polynomials or RBFs.
ContributorsLiu, Tony (Author) / Platte, Rodrigo B (Thesis advisor) / Renaut, Rosemary (Committee member) / Kaspar, David (Committee member) / Moustaoui, Mohamed (Committee member) / Motsch, Sebastien (Committee member) / Arizona State University (Publisher)
Created2019