Automatic Differentiation of Algorithms

TRAMA

Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development.Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming (i.e., use of adjoints in optimization) and implementation (i.e., memory management problems). A strong theme of the book is the relationships between AD tools and other software tools, such as compilers and parallelizers. A rich variety of significant applications are presented as well, including optimum-shape design problems, for which AD offers more efficient tools and techniques.

SOMMARIO

Part titles: Invited Contributions.- Parameter Identification and Least Squares.- Applications in Ode's and Optimal Control.- Applications in PDE's.- Applications in Science and Engineering.- Maintaining and Enhancing Parallelism.- Exploiting Structure and Sparsity.- Space-Time Tradeoffs in the Reverse Mode.- Use of Second and Higher Derivatives.- Error Estimates and Inclusions.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780387953052
• Dimensioni: 235 x 155 mm
• Formato: Copertina rigida
• Illustration Notes: XXVII, 432 p. 84 illus.
• Pagine Arabe: 432
• Pagine Romane: xxvii