ISBN: 9780898716597 - evaluating derivatives: principles and techniques of algorithmic differentiation (16 risultati)

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Condizione: New. A comprehensive treatment of algorithmic, or automatic, differentiation. Num Pages: 460 pages, ill. BIC Classification: PBKJ; PBWH. Category: (P) Professional & Vocational. Dimension: 256 x 159 x 22. Weight in Grams: 818. . 2008. Second. Paperback. . . . .

Paperback. Condizione: Brand New. 2nd edition. 460 pages. 9.75x6.75x0.75 inches. In Stock.

Paperback. Condizione: New. Second Edition. 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. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. There is also added material on checkpointing and iterative differentiation. To improve readability the more detailed analysis of memory and complexity bounds has been relegated to separate, optional chapters. 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.…

Condizione: As New. Unread book in perfect condition.

Condizione: New. A comprehensive treatment of algorithmic, or automatic, differentiation. Num Pages: 460 pages, ill. BIC Classification: PBKJ; PBWH. Category: (P) Professional & Vocational. Dimension: 256 x 159 x 22. Weight in Grams: 818. . 2008. Second. Paperback. . . . . Books ship from the US and Ireland.…

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Condizione: New. This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity. There is also added m.

Taschenbuch. Condizione: Neu. Neuware - 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. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. There is also added material on checkpointing and iterative differentiation. To improve readability the more detailed analysis of memory and complexity bounds has been relegated to separate, optional chapters. 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.…

Paperback. Condizione: New. Second Edition. 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. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. There is also added material on checkpointing and iterative differentiation. To improve readability the more detailed analysis of memory and complexity bounds has been relegated to separate, optional chapters. 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.…