Automatic Differentiation of Algorithms: From Simulation to Optimization
Editat de George Corliss, Christele Faure, Andreas Griewank, Laurent Hascoet, Uwe Naumannen Limba Engleză Hardback – 8 ian 2002
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.
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Springer – 27 ian 2014 | 322.76 lei 6-8 săpt. | |
Hardback (1) | 326.34 lei 6-8 săpt. | |
Springer – 8 ian 2002 | 326.34 lei 6-8 săpt. |
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Specificații
ISBN-13: 9780387953052
ISBN-10: 0387953051
Pagini: 432
Ilustrații: XXVII, 432 p. 84 illus.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.75 kg
Ediția:2002
Editura: Springer
Colecția Springer
Locul publicării:New York, NY, United States
ISBN-10: 0387953051
Pagini: 432
Ilustrații: XXVII, 432 p. 84 illus.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.75 kg
Ediția:2002
Editura: Springer
Colecția Springer
Locul publicării:New York, NY, United States
Public țintă
Professional/practitionerCuprins
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.