Poster Session/Reception

Monday Evening, February 12, 1996
5:15 PM - 7:00 PM

Poster presenters are expected to stand by their posters. (Posters may be put up Monday morning and may remain through Thursday afternoon.)

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
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

Back to Computational Differentiation Home Page

MEM, 12/28/95