Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second EditionThis title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. |
Contents
| 1 | |
| 5 | |
| 11 | |
| 16 | |
1 | 29 |
Memory Issues and Complexity Bounds | 61 |
Implementation and Software | 107 |
Sparse Forward and Reverse | 145 |
Exploiting Sparsity by Compression | 163 |
Other editions - View all
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation Andreas Griewank No preview available - 1987 |
Common terms and phrases
adjoint procedure adouble algorithm apply argument arithmetic operations assume assumption automatic differentiation bound calculation chain rule Chapter checkpointing column complexity components compression computational graph consider control flow graph corresponding cost defined dependent differentiation directional derivatives domain edges elemental functions elimination equation evaluation procedure exactly example Exercise F(zk Fixed Point Iteration floating point forward and reverse forward mode forward sweep function F gradient Hence Hessian implementation incremental independent variables intermediate iteration Jacobian Jacobian F'(x Laurent linear Lipschitz continuous listed in Table loop Markowitz matrix methods minimal multiplications Newton's method nonincremental nonlinear nonzero obtain OpenMP operations count operator overloading optimal original overwriting partial polynomial problem propagation Proposition recursion reduce respect result return sweep reverse mode runtime scalar second-order adjoint sparse sparsity tangent tape Taylor coefficients Taylor polynomials tensor univariate v₁ values vertices Vi-n yields zero