Journal Article10.1080/0025570X.2002.11953128
Doubly Recursive Multivariate Automatic Differentiation
TL;DR: The purpose of this paper is to present the recursive automatic differentiation system, an amazingly elegant extension of the one-variable/one-derivative system that handles essentially any number of variables and derivatives that is recursively defined.
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Abstract: system. That's right. The automatic differentiation system never formulates a symbolic expression for the derivative. Automatically calling on something like Mathematica to produce a symbolic derivative, and then plugging in a value for x is the wrong image entirely. Automatic differentiation is something completely different. Well OK, but so what? Symbolic algebra systems are so prevalent and powerful today, why should we be concerned with avoiding symbolic methods? There are two answers. The first is practical. Symbolic generation of derivatives can lead to exponential growth in the length of expressions. That causes computational problems in real applications. Accordingly, there is a practical applied side to the subject of automatic differentiation, as witnessed by the serious attention of computer scientists and numerical analysts [3, 4]. The second answer is more mathematical. It is a relatively easy task to create a single variable automatic differentiation system capable of evaluating first derivatives. In fact, writing in this MAGAZINE in 1986, Rall [10] gives a beautiful presentation of just such a system. What is mathematically interesting is an amazingly elegant extension of the one-variable/one-derivative system that handles essentially any number of variables and derivatives. The extension is recursively defined, employing an induction on both the number of variables and the number of derivatives, and using fundamental definitions that are virtually identical to the ones used in Rall's system. The purpose of this paper is to present the recursive automatic differentiation system. To set the stage, we will begin with a brief review of Rall's one-variable/onederivative system, followed by an example of the recursive system in action. Then the mathematical formulation of the recursive system will be presented. The paper will end with a brief discussion of practical issues related to the recursive system.
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Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
Andreas Griewank,Andrea Walther +1 more
- 01 Jan 1987
TL;DR: This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity.
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An efficient method for the numerical evaluation of partial derivatives of arbitrary order
TL;DR: The key ideas are a hyperpyramid data structure and a generalized Leibniz's rule which produces any partial derivative by forming the minimum number of products (between two lower partials) together with a product of binomial coefficients.
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Structured second-and higher-order derivatives through univariate Taylor series
TL;DR: This work computes derivatives in a fashion that parallelizes well, exploits sparsity or other structure frequently found in Hessian matrices, can compute only selected elements of a Hessian matrix, and computes Hessian × vector products.
54
A recursive approach to multivariate automatic differentiation
Dan Kalman,Robert Lindell +1 more
TL;DR: In this paper, a recursive approach to define the necessary operations in the context of functions of several variables is presented, where the definitions are essentially the same as those needed in the single variable case.