The Art of Differentiating Computer Programs - An Introduction to Algorithmic Differentiation

By Uwe Naumann. Software, environments, tools, 2012

335 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/0a975e05e368c62d68d3d496b81ce9916f4dedbb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Numerical Analysis

Citations in the corpus

CasADi: a software framework for nonlinear optimization and optimal control (2018, methodology): “We refer to [5, Chapter 3] for a simple introduction and Griewank and Walther [60] or Naumann [106] for a more complete introduction to AD.”
JuMP: A Modeling Language for Mathematical Optimization (2015, methodology): “This composition of forward-mode AD with reverse-mode AD is known as forwardover-reverse mode [41, 64], and JuMP implements it by manually augmenting the reverse-mode implementation to propagate the required infinitesimal components for directional derivatives.”
The Tapenade automatic differentiation tool: Principles, model, and specification (2013, methodology): “Other invaluable sources are the Automatic Differentiation Web site,1 the reference book [Griewank and Walther 2008], and the tool-development oriented book [Naumann 2012].”
OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization (2019, methodology): “Algorithmic differentiation (AD) uses a software tool to parse the code of an analysis tool to produce new code that computes derivatives of that analysis [38, 76].”
Effective adjoint approaches for computational fluid dynamics (2019, background): “The details of how the differentiated function is constructed by using the chain rule can be found in AD textbooks [17, 150].”
Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013, methodology): “2 Algorithmic Differentiation Algorithmic differentiation (AD)—also known as computational differentiation or automatic differentiation— is a well-known method based on the systematic application of the differentiation chain rule to computer programs [29, 66].”