December 4, 2009
Laurie Cavey, Wed. December 9, 2:40-3:30 pm, MG 120.
Developing Students’ Understanding of Mathematical Definitions: Why Bother?
Definitions are a fundamental part of doing mathematics, yet studies indicate that many students struggle to learn and apply definitions. In fact, many instructors wonder (myself included) how students can misapply definitions that are so clearly stated. Part of the issue is that a student’s previous mathematical experiences influence how she thinks, even when encountering a new idea that is seemingly unrelated. Not knowing what these experiences might entail, it can be difficult to know how to help students develop a better understanding of a particular definition. So, why bother? I will provide a brief overview of the research in this area including an instructional strategy (student generated examples) that may influence the way we think about developing students’ understanding of definitions.
November 30, 2009
Marion Scheepers, Wed. December 2, 2:40-3:30 pm, MG 120.
Online shopping and banking, Wireless communication and remote control devices have become common place. Nontrivial computing power and scanning devices of high power have become readily available. This creates an environment in which information in transit can be easily accessed or changed by unknown parties.
Cryptography is the main tool used to keep information secure. In this talk we will give a brief, motivated, outline of some of the mathematical foundations of cryptography. We also give an example to illustrate that mere possession of a good crypto-system does not guarantee security – one must also use it right.
November 11, 2009
Grady Wright, Wed. November 18, 2:40-3:30 pm, MG 120.
Scattered Node Finite Difference-Type Formulas Generated from Radial Basis Functions with Applications
In the finite difference (FD) method for solving partial differential equations (PDEs), derivatives at a node are approximated by a weighted sum of function values at some surrounding nodes. In the one dimensional case, the weights of the FD formulas are conveniently computed using polynomial interpolation. These one dimensional formulas can be combined to create FD formulas for partial derivatives in two and higher dimensions. This strategy, however, requires that the nodes of the FD “stencils” are situated on some kind of structured grid (or collection of structured grids), which severely limits the application of the FD method to PDEs in irregular geometries. In this talk, we present a novel approach that resolves this problem by allowing the nodes of the FD stencils to be placed freely and by using radial basis function (RBF) interpolation for computing the corresponding weights in the scattered node FD-type formulas. We show how this RBF approach can exactly reproduce all classical FD formulas and how compact FD formulas can be generalized to scattered nodes and RBFs. This latter result is important in that it allows the number of nodes in the stencils to remain relatively low without sacrificing accuracy. For the Poisson equation, these new compact scattered node schemes can also be made diagonally dominant, which ensures both a high degree of robustness and applicability of iterative methods. We conclude the talk with some numerical examples and future applications of the method for geophysical problems.
November 3, 2009
Leming Qu, Wed. November 11, 2:40-3:30 pm, MG 120.
Wavelet Image Restoration and Regularization Parameters Selection
For the restoration of an image based on its noisy distorted observations, we propose wavelet domain restoration by a scale-dependent penalized regularization method (WaveRSL1). The data-adaptive choice of the regularization parameters is based on the Akaike Information Criterion (AIC) and the degrees of freedom (df) are estimated by the number of nonzero elements in the solution. Experiments on some commonly used testing images illustrate that the proposed method possesses good empirical properties.
October 21, 2009
Jens Harlander, Wed. October 28, 2:40-3:30 pm, MG 120.
Introduction to Computational Complexity
Complexity theory provides ways of measuring the difficulty of computational mathematics problems. Some problems are indeed impossibly difficult (your Math 108 and 143 students are right after all!). For example, there does not exist an algorithm that decides whether a polynomial (in an arbitrary number of variables) with integer coefficients has integer roots. However for many difficult problems, simple strategies work well in practice as long as one is willing to ignore a hopefully sparse set of inputs. I will discuss basic features of the theory, give you more examples of impossibly hard problems and tell you about the relevance of all of this to Internet security.
October 20, 2009
Here is the list of speakers for the rest of the term.