1st Tuesday Journal-Paper Club
for the second 1TJC meeting of 2016 we return to the German Club in East Brisbane.
Andrew Smith has kindly agreed/been persuaded to be our reader leader on an interesting topic:
Rall, Louis B., and George F. Corliss. "An introduction to automatic differentiation." Computational Differentiation: Techniques, Applications, and Tools (1996): 1-17.
http://www.eng.mu.edu/corlissg/Pubs/Papers/1996d.ps.gz
About the 1st Tuesday Journal-Paper Club: the idea is to meet regularly, usually on the 1st Tuesday of the month as the name suggests (inspired by the ABC TV series "1st Tuesday Book Club"). Each month, the participants would agree on a highly cited, 'top ten' or major-prize-winning article in an SPS or ComSoc journal (but not one of our own!). We would also select a Discussion Leader. Through the month, each of the participants would read the article. At the next meeting, the Discussion Leader would lead a discussion of that article, starting with his/her own appraisal. In this way, it is hoped that we could all broaden our understanding of the field and further develop a sense of community.
1st rule of 1st Tuesday Journal-Paper Club: tell everyone about 1st Tuesday Journal-Paper Club.
Date and Time
Location
Hosts
Registration
- Date: 01 Mar 2016
- Time: 08:00 AM UTC to 10:00 AM UTC
-
Add Event to Calendar
- 416 Vulture St
- East Brisbane, Queensland
- Australia 4102
- Building: Brisbane German Club
- Room Number: Bar area
- Contact Event Host
-
We will also be looking for suggestions for papers to discuss and volunteer reader leaders for the 2016 schedule...
- Starts 09 February 2016 08:00 PM UTC
- Ends 01 March 2016 02:00 AM UTC
- No Admission Charge
Agenda
Abstract
This paper provides a gentle introduction to the field of automatic differentiation (AD), with the goal of equipping the reader for the other papers in this book. AD is the systematic application of the familiar rules of calculus to computer programs, yielding programs for the propagation of numerical values of first, second, or higher derivatives. AD can be regarded as traversing the code list (or computational graph) in the forward mode, the reverse mode, or a combination of the two. Algorithms for numerical optimization, differential equations, and interval analysis all could use AD technology to compute the required derivatives. AD typically is implemented by using either source code transformation or operator overloading. We give examples of code for each. Finally, we outline some 'pitfalls of AD for naive users, and we present opportunities for future research.