Lesson plan
Automatic differentiation
In mathematics and computer algebra, automatic differentiation (auto-differentiation, autodiff, or AD), also called algorithmic differentiation, computational differentiation, and differentiation arithmetic is a set of techniques to evaluate the partial derivative of a function specified by a computer program. Automatic differentiation enables the simultaneous computation of the numerical values of arbitrarily complex functions and their derivatives using only the function itself; no symbolic derivative is required. Auto-differentiation is thus neither numeric nor symbolic, nor is it a combination of both. In contrast to the more traditional numerical methods based on finite differences, auto-differentiation is 'in theory' exact, and in comparison to symbolic algorithms, it is computationally inexpensive. Automatic differentiation exploits the fact that every computer calculation, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.) and elementary functions (exp, log, sin, cos, etc.). By applying the chain rule repeatedly to these operations, partial derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor of more arithmetic operations than the original program.
Article summary
In mathematics and computer algebra, automatic differentiation (auto-differentiation, autodiff, or AD), also called algorithmic differentiation, computational differentiation, and differentiation arithmetic is a set of techniques to evaluate the partial derivative of a function specified by a computer program. Automatic differentiation enables the simultaneous computation of the numerical values of arbitrarily complex functions and their derivatives using only the function itself; no symbolic derivative is required. Auto-differentiation is thus neither numeric nor symbolic, nor is it a combination of both. In contrast to the more traditional numerical methods based on finite differences, auto-differentiation is 'in theory' exact, and in comparison to symbolic algorithms, it is computationally inexpensive. Automatic differentiation exploits the fact that every computer calculation, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.) and elementary functions (exp, log, sin, cos, etc.). By applying the chain rule repeatedly to these operations, partial derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor of more arithmetic operations than the original program.
Key claims
- publication date: 1976 — source: Wikipedia · confidence 0.95
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- The record states that the publication date is "1976". What evidence would count against this claim?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
- Wikipedia https://en.wikipedia.org/w/index.php?oldid=1373407057
Key claims and bibliography are generated from the same tracked claims and sources as the article page.