Center for Approximation Theory

Center for Approximation Theory

To discover and analyze simple, easy to work with, concrete quantities that can do a good, efficient job in their place, for example, splines to fit messy curves, wavelets to analyze noisy signals and to compress large images, and radial basis functions to fit scattered data and serve as the “approximation engine” of neural networks.

Engineering, finance, science, and many areas of mathematics make use of quantities that are too complicated, too difficult, and even too abstract to work with directly. Areas of faculty interest in the Center for Approximation Theory include approximations by orthogonal polynomials, radial basis functions, and wavelets; further topics of interest include scattered data surface fitting, rates of approximation, constrained approximation, polynomial inequalities, orthogonal polynomials, wavelets, splines, and non-linear

College of Science
Phone: 979.845.6028