Hostname: page-component-77c89778f8-vpsfw Total loading time: 0 Render date: 2024-07-19T04:01:33.807Z Has data issue: false hasContentIssue false
  • English
  • Français

La différentiation automatique et son utilisation en optimisation

Published online by Cambridge University Press:  17 May 2008

Jean-Pierre Dussault
Jean-Pierre Dussault*
Affiliation:
Département d'informatique, Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec J1K 2R1, Canada; Jean-Pierre.Dussault@USherbrooke.ca
Get access

Abstract

In this work, we present an introduction to automatic differentiation, its use in optimization software, and some new potential usages. We focus on the potential of this technique in optimization. We do not dive deeply in the intricacies of automatic differentiation, but put forward its key ideas. We sketch a survey, as of today, of automatic differentiation software, but warn the reader that the situation with respect to software evolves rapidly. In the last part of the paper, we present some potential future usage of automatic differentiation, assuming an ideal tool is available, which will become true in some unspecified future.

Keywords

Différentiation automatiquealgorithmes numériques d'optimisation.
Type
Research Article
Information
RAIRO - Operations Research , Volume 42 , Issue 2: CIRO 05 , April 2008 , pp. 141 - 155
Copyright
© EDP Sciences, ROADEF, SMAI, 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Dussault, J.-P., High order Newton-penalty Algorithms. J. Comput. Appl. Math. 182 (2005) 117–133. CrossRef
J.-P. Dussault and B. Hamelin, Implementation of high order Newton Algorithms, in Scilab first international conference, Dec. 2004.
J.-P. Dussault and B. Hamelin, Robust descent in differentiable optimization using automatic finite differences. Optimization Methods and Software (2005). À paraître.
A. Griewank, On automatic differentiation, in Mathematical Programming: Recent Developments and Applications, edited by M. Iri and K. Tanabe, Kluwer Academic Publishers (1989) 83–108.
Griewank, A., Achieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation. Optimization Methods and Software 1 (1992) 35–54. CrossRef
A. Griewank, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Frontiers in Appl. Math. 19 SIAM, Philadelphia, PA (2000).
J.J. Moré, Automatic differentiation tools in optimization software, in Automatic Differentiation of Algorithms: From Simulation to Optimization, Computer and Information Science, Springer, New York, NY (2001) Chapter 2, 25–34.