La différentiation automatique et son utilisation en optimisation

Jean-Pierre Dussault

RAIRO - Operations Research (2008)

  • Volume: 42, Issue: 2, page 141-155
  • ISSN: 0399-0559

Abstract

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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. 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.

How to cite

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Dussault, Jean-Pierre. "La différentiation automatique et son utilisation en optimisation." RAIRO - Operations Research 42.2 (2008): 141-155. <http://eudml.org/doc/250392>.

@article{Dussault2008,
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. },
author = {Dussault, Jean-Pierre},
journal = {RAIRO - Operations Research},
keywords = {Différentiation automatique; algorithmes numériques d'optimisation.; automatic differentiation; optimization},
language = {eng},
month = {5},
number = {2},
pages = {141-155},
publisher = {EDP Sciences},
title = {La différentiation automatique et son utilisation en optimisation},
url = {http://eudml.org/doc/250392},
volume = {42},
year = {2008},
}

TY - JOUR
AU - Dussault, Jean-Pierre
TI - La différentiation automatique et son utilisation en optimisation
JO - RAIRO - Operations Research
DA - 2008/5//
PB - EDP Sciences
VL - 42
IS - 2
SP - 141
EP - 155
AB - 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.
LA - eng
KW - Différentiation automatique; algorithmes numériques d'optimisation.; automatic differentiation; optimization
UR - http://eudml.org/doc/250392
ER -

References

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  1. J.-P. Dussault, High order Newton-penalty Algorithms. J. Comput. Appl. Math.182 (2005) 117–133.  Zbl1077.65061
  2. J.-P. Dussault and B. Hamelin, Implementation of high order Newton Algorithms, in Scilab first international conference, Dec. 2004.  
  3. J.-P. Dussault and B. Hamelin, Robust descent in differentiable optimization using automatic finite differences. Optimization Methods and Software (2005). À paraître.  Zbl1112.90079
  4. 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.  Zbl0696.65015
  5. A. Griewank, Achieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation. Optimization Methods and Software1 (1992) 35–54.  
  6. A. Griewank, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Frontiers in Appl. Math. 19 SIAM, Philadelphia, PA (2000).  Zbl0958.65028
  7. 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.  

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