Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition
This 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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Jacobian and Hessian Accumulation
Observations on Efficiency
Reversal Schedules and Checkpointing
Taylor and Tensor Coefficients
Differentiation without Differentiability
Implicit and Iterative Differentiation
Going beyond Forward and Reverse
Other editions - View all
accumulation additions adjoint analysis apply approach argument arithmetic assignment assume assumption bound calculation Chapter checkpointing coefficients column complexity components compression computational graph condition consider constant corresponding cost count defined dependent derivative determine difference differentiation directional discussed domain double edges elemental elimination equation evaluation procedure exactly example Exercise fact factor Figure forward function given gradient Hence Hessian implementation incremental independent initial intermediate iteration Jacobian linear listed matrix memory methods minimal multiplications nonlinear Note obtain occur once operations optimal original partial particular performed problem propagation Proposition recording reduce relation represent requires respect result reverse mode rule runtime separability simple solution statements steps structure sweep Table tangent task Taylor tool transformation tree values variables vector yields zero