Complimentary hors d'oeuvres and Beverages will be available.

A Comparison of Automatic Differentiation Techniques for Computing Hessians

Jason Abate and Chris Bischof, Argonne National Laboratory; and Alan Carle, Rice University

ADIFOR 2.0

Alan Carle, Rice University

Synthetic Calculus in X-Ray System Design

Jacob Beutel and Daniel Mickish, E.I. DuPont de Nemours & Company

Nondestructive Computational Differentiation Eric J. Braude, Boston University

AD is not always A

Alan Carle and Mike Fagan, Rice University

A Neo-Finite-Difference Method for Electromagnetic Simulations

K. R. Chen, National ChangHua University of Education, Taiwan; and J. M. Dawson, University of California, Los Angeles

Differentiation-Guided Parallel Code Generation for Simulation of Multibody Rigid Systems

Laurent Cogne, IRISA/INRIA, France

Giving Reverse Differentiation a Helping Hand

B. Christianson, A. Davies, Lawrence.C.W. Dixon, and R. Roy, University of Hertfordshire, United Kingdom

A New Proof of Faa' de Bruno's Formula

L. Hernandez Encinas, Universidad de Salamanca, Spain; F. Montoya Vitini, and J. Munoz Masque, CSIC -- Institute de Fisica Aplicada, Spain

A Code Generator for Automatic Differentiation of Algorithms Written in C/C++

Ilan Finci, Ziv Yaniv, and Michel Bercovier, Hebrew University, Israel

Complexity of the Derivative

Herbert Fischer, Technische Universitat Munchen, Germany

Power Series Solutions of ODEs

Harley Flanders, University of Michigan, Ann Arbor

Case Studies of Choosing a Numerical Differentiation Method Under Uncertainty: Computer-Aided Design and Radiotelescope Network Design

Andrei M. Finkelstein, Russian Academy of Sciences, Russia; and Misha Koshelev, University of Texas, El Paso

Rational Predictors based on AD for ODE Solving

Andreas Griewank and Petra Henneberger, Technical University of Dresden, Germany

Automatic Differentiation of Parallel Languages

Paul Hovland, University of Illinois, Urbana

Parallelizing Time in a Leapfrog Scheme

Christian Bischof, Argonne National Laboratory; Timothy L. Knauff Jr., University of Illinois, Urbana; and Po-Ting Wu, Argonne National Laboratory

Computational Methods for Design Sensitivity Analysis of Optimal Control Problems

Narendra N. Kota and Jasbir S. Arora, University of Iowa

Combining Numerical Differentiation and Monte Carlo Results in Faster Error Estimation

Vladik Kreinovich, University of Texas, El Paso; Vladimir Dmitriev and Nina Zheludeva, St. Petersburg, Russia

Exponentiation in Power Series Fields

Salma Kuhlmann, Universitat Heidelberg, Germany

Economic Planning via Optimal Control - Synthetic Calculus Applied to Resource Management

James M. McDonough, University of Kentucky

MXYZPTLK, Version 4.0. A C++ Implementation of Automated Differentiation and Differential Algebra

Leo Michelotti, Fermi National Accelerator Laboratory

Mathematica Series as a Computational Differentiation Tool

Richard D. Neidinger, Davidson College

Higher-Order Sensitivity Analysis of Mechanical Structure by Differential Algebraic Method

Ikuo Ozaki and Fumihiko Kimura, University of Tokyo, Japan; and Martin Berz, Michigan State University

ADIC: A Robust Tool for Automatic Differentiation of C

Christian Bischof, Andrew Mauer, and Lucas Roh, Argonne National Laboratory

Automatic Differentiation of the CFL3D Flow Code in Incremental Iterative Form

A. C. Taylor III, Lawrence L. Green, P.A. Newman, NASA Langley Research Center

Numerical Integration of ODE's and Chaos-Integrability Transition

Alexander Tovbis, West Virginia University

Evaluating Higher Derivative Tensors through Univariate Taylor Polynomials

Andreas Griewank and Jean Utke, Technical University of Dresden, Germany

Dependency Graph, Reverse Differentiation and Functions: Problems and Tools

Dominique Villard, IRISA, France

Wind, Pressure and Thermodynamic Retrieval from Single-Dopplar Radar Data Using a 3-D Compressible Storm-Scale Model

Zhi Wang and Kelvin Droegemeier, University of Oklahoma, Norman

A Generalization of Le Dimet and Ngodock's Optimal Sensitivity Analysis of Physical Parameters in the Presence of Data

Zhi Wang, L. White, and K. Droegemeier, University of Oklahoma, Norman

A Node Elimination Rule for the Calculation of Jacobian Matrices

Toshinobu Yoshida, University of Electro-Communications, Japan

The Efficient Computation of Sparse Jacobians Using Automatic Differentiation

Thomas F. Coleman and Arun Verma, Cornell University

Computational Differentiation in BVPs in Ordinary Differential Equations

Bart Childs, Texas A&M University; and Tim McGuire, Pillsbury College

The Cholesky Decomposition and its Derivatives

Stephen P. Smith, EA Engineering, Science, and Technology

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