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Title: Automatic differentiation bibliography

Abstract

This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.

Authors:
 [1]
  1. comp.
Publication Date:
Research Org.:
Argonne National Lab., IL (United States). Mathematics and Computer Science Div.
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
10178160
Report Number(s):
ANL/MCS-TM-167
ON: DE92040599
DOE Contract Number:
W-31109-ENG-38
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: Jul 1992
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DIFFERENTIAL CALCULUS; AUTOMATION; ALGORITHMS; DIFFERENTIAL EQUATIONS; BIBLIOGRAPHIES; 990200; MATHEMATICS AND COMPUTERS

Citation Formats

Corliss, G.F. Automatic differentiation bibliography. United States: N. p., 1992. Web. doi:10.2172/10178160.
Corliss, G.F. Automatic differentiation bibliography. United States. doi:10.2172/10178160.
Corliss, G.F. Wed . "Automatic differentiation bibliography". United States. doi:10.2172/10178160. https://www.osti.gov/servlets/purl/10178160.
@article{osti_10178160,
title = {Automatic differentiation bibliography},
author = {Corliss, G.F.},
abstractNote = {This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.},
doi = {10.2172/10178160},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jul 01 00:00:00 EDT 1992},
month = {Wed Jul 01 00:00:00 EDT 1992}
}

Technical Report:

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