Title: Survey of Automatic Differentiation

Automatic differentiation is a technique for providing fast, accurate values of derivative objects (gradients, Hessians, Taylor series) required by modern tools for optimization, nonlinear systems, differential equations, or sensitivity analysis. We outline some needs and applications for derivatives, survey the functionality of AD in forward and reverse modes, and discuss some of the mathematical and computer science challenges of AD.
This talk should be accessible after first semester calculus and a programming course in data structures. It should be interesting to researchers in symbolic computation or in scientific or engineering applications requiring almost any techniques from numerical analysis.
Short Bio:
Dr. George Corliss, Marquette University, Milwaukee WI, holds faculty appointments in Engineering, in Arts & Sciences, and in Business. He is a researcher in scientific computation, especially in interval techniques and automatic differentiation. He is a veteran teacher of computer science and engineering topics, especially their applications, with considerable industrial mathematics and computing experience. He directs a masters program catering to working IT professionals.