.png){kind=icon}
.png){kind=icon}
Return
An introduction to algorithmic differentiation
.png){kind=icon}
OA
AI
DOI：10.1002/widm.1334.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Algorithmic differentiation (AD), also known as automatic differentiation, is a technology for accurate and efficient evaluation of derivatives of a function given as a computer model. The evaluations of such models are essential building blocks in numerous scientific computing and data analysis applications, including optimization, parameter identification, sensitivity analysis, uncertainty quantification, nonlinear equation solving, and integration of differential equations. We provide an introduction to AD and present its basic ideas and techniques, some of its most important results, the implementation paradigms it relies on, the connection it has to other domains including machine learning and parallel computing, and a few of the major open problems in the area. Topics we discuss include: forward mode and reverse mode of AD, higher-order derivatives, operator overloading and source transformation, sparsity exploitation, checkpointing, cross-country mode, and differentiating iterative processes. This article is categorized under: Algorithmic Development > Scalable Statistical Methods Technologies > Data Preprocessing
Keywords:
adjoints
algorithmic differentiation
automatic differentiation
backpropagation
checkpointing
sensitivities
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.7
Papers:
532
Citations:
5.3K