Georgia Institute of Technology College of Sciences

An important question in modern complex analysis is to obtain a characterization of the sequence of points in the disc {z_j} that interpolates any given target sequence {a_j} with an element of a space of analytic functions. In this talk we will discuss this question and reformulate it as a problem in linear algebra and then show how this can be solved with relatively straightforward tools. Connections to open questions will also be given.

Many derivatives products are directly or indirectly associated with integrated diffusion processes. We develop a general perturbation method to price those derivatives. We show that for any positive diffusion process, the hitting time of its integrated process is approximately normally distributed when the diffusion coefficient is small. This result of approximate normality enables us to reduce many derivative pricing problems to simple expectations. We illustrate the generality and accuracy of this probabilistic approach with several examples, with emphasis on timer options. Major advantages of the proposed technique include extremely fast computational speed, ease of implementation, and analytic tractability.

A presentation by Wing Li (Math, former NSF program officer) and Marion Usselman (Associate Director of CEISMC).

I mainly discuss the following problem: given a set of scattered locations and nonnegative values, how can one construct a smooth interpolatory or fitting surface of the given data? This problem arises from the visualization of scattered data and the design of surfaces with shape control. I shall start explaining scattered data interpolation/fitting based on bivariate spline functions over triangulation without nonnegativity constraint. Then I will explain the difficulty of the problem of finding nonnegativity perserving interpolation and fitting surfaces and recast the problem into a minimization problem with the constraint. I shall use the Uzawa algorithm to solve the constrained minimization problem. The convergence of the algorithm in the bivariate spline setting will be shown. Several numerical examples will be demonstrated and finally a real life example for fitting oxygen anomalies over the Gulf of Mexico will be explained.