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Algorithmic Differentiation in Theory and in Practice
Optimization| Speaker: | Bradley M. Bell, University of Washington |
| Related Webpage: | https://www.seanet.com/~bradbell/home.htm |
| Location: | 2112 MSB |
| Start time: | Mon, Oct 29 2018, 2:10PM |
We refer to the automatic creation of an algorithm that computes derivative values from an algorithm that computes function values as Algorithmic Differentiation, also referred to as Automatic Differentiation and abbreviated as AD. A brief introduction to AD can be found in http://en.wikipedia.org/wiki/Automatic_differentiation.
The web site http://www.autodiff.org is dedicated to research about, and promoting the use of, AD. This talk will review the theory of AD and how it is accomplished. Then we will discuss a particular AD package and what features it has. Finally we will present an application of AD to non-linear mixed effects modeling.