Evaluating derivatives principles and techniques of algorithmic differentiation PDF下载

作者: Andreas Griewank / Andrea Walther
出版社: Society for Industrial and Applied Mathematic
副标题: Principles and Techniques of Algorithmic Differentiation, Second Edition
出版年: 2008-09-26
页数: 460
定价: USD 73.50
装帧: Paperback
ISBN: 9780898716597

Algorithmic, or automatic, differentiation (AD) is a growing area of theoretical research and software development concerned with the accurate and efficient evaluation of derivatives for function evaluations given as computer programs. The resulting derivative values are useful for all scientific computations that are based on linear, quadratic, or higher order approximations to nonlinear scalar or vector functions.
AD has been applied in particular to optimization, parameter identification, nonlinear equation solving, the numerical integration of differential equations, and combinations of these. Apart from quantifying sensitivities numerically, AD also yields structural dependence information, such as the sparsity pattern and generic rank of Jacobian matrices. The field opens up an exciting opportunity to develop