Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation Griewank AndreasPaperback
Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation Griewank AndreasPaperback Algorithmic, or automatic, differentiation (AD) is a growing area of theoretical research…
Specifikacia Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation Griewank AndreasPaperback
Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation Griewank AndreasPaperback
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. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. 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.
To improve readability the more detailed analysis of memory and complexity bounds has been relegated to separate, optional chapters. There is also added material on checkpointing and iterative differentiation. The book consists of: a stand-alone introduction to the fundamentals of AD and its software; a thorough treatment of methods for sparse problems; and final chapters on program-reversal schedules, higher derivatives, nonsmooth problems and iterative processes.