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Advanced Data Assimilation for GeosciencesLecture Notes of the Les Houches School of Physics: Special Issue, June 2012$
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Éric Blayo, Marc Bocquet, Emmanuel Cosme, and Leticia F. Cugliandolo

Print publication date: 2014

Print ISBN-13: 9780198723844

Published to Oxford Scholarship Online: March 2015

DOI: 10.1093/acprof:oso/9780198723844.001.0001

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Adjoints by automatic differentiation

Adjoints by automatic differentiation

Chapter:
(p.349) 15 Adjoints by automatic differentiation
Source:
Advanced Data Assimilation for Geosciences
Author(s):

L. Hascoët

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198723844.003.0015

This chapter describes how adjoint algorithms can be created by automatic differentiation (AD). Data assimilation makes intensive use of gradients. In many situations, the so-called adjoint approach is generally the most efficient way to compute gradients, by propagating derivatives backwards from the result of the given model or function. Writing an adjoint algorithm by hand is a complex, error-prone task. When the given model is provided in the form of a computer algorithm, AD can build its adjoint algorithm mechanically, for instance by program transformation. This chapter presents the principles of AD, focusing on the adjoint mode. It provides a brief panorama of existing AD tools, and the program analysis and compiler technology that they employ to produce efficient adjoint algorithms.

Keywords:   automatic differentiation, program transformation, compiler, program analysis, adjoint algorithm, gradient

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