evaluating derivatives principles and techniques of algorithmic differentiation

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Evaluating Derivatives

Author : Andreas Griewank
ISBN : 9780898717761
Genre : Mathematics
File Size : 38. 38 MB
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This title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity.

Evaluating Derivatives

Author : Andreas Griewank
ISBN : 9780898716597
Genre : Mathematics
File Size : 77. 30 MB
Format : PDF, ePub, Docs
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This title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity.

Evaluating Derivatives

Author : Andreas Griewank
ISBN : 0898714516
Genre : Mathematics
File Size : 55. 12 MB
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Covers the fundamentals of AD and its software, methods for sparse problems, higher derivatives, nonsmooth problems, and program reversal schedules.

Automatic Differentiation Of Algorithms

Author : George Corliss
ISBN : 9781461300755
Genre : Computers
File Size : 76. 57 MB
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A survey book focusing on the key relationships and synergies between automatic differentiation (AD) tools and other software tools, such as compilers and parallelizers, as well as their applications. The key objective is to survey the field and present the recent developments. In doing so the topics covered shed light on a variety of perspectives. They reflect the mathematical aspects, such as the differentiation of iterative processes, and the analysis of nonsmooth code. They cover the scientific programming aspects, such as the use of adjoints in optimization and the propagation of rounding errors. They also cover "implementation" problems.

The Art Of Differentiating Computer Programs

Author : Uwe Naumann
ISBN : 1611972078
Genre : Automatic differentiations
File Size : 39. 99 MB
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This is the first entry-level book on algorithmic (also known as automatic) differentiation (AD), providing fundamental rules for the generation of first- and higher-order tangent-linear and adjoint code. The author covers the mathematical underpinnings as well as how to apply these observations to real-world numerical simulation programs. Readers will find: examples and exercises, including hints to solutions; the prototype AD tools dco and dcc for use with the examples and exercises; first- and higher-order tangent-linear and adjoint modes for a limited subset of C/C++, provided by the derivative code compiler dcc; a supplementary website containing sources of all software discussed in the book, additional exercises and comments on their solutions (growing over the coming years), links to other sites on AD, and errata.

Advances In Automatic Differentiation

Author : Christian H. Bischof
ISBN : 9783540689423
Genre : Computers
File Size : 46. 74 MB
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The Fifth International Conference on Automatic Differentiation held from August 11 to 15, 2008 in Bonn, Germany, is the most recent one in a series that began in Breckenridge, USA, in 1991 and continued in Santa Fe, USA, in 1996, Nice, France, in 2000 and Chicago, USA, in 2004. The 31 papers included in these proceedings re?ect the state of the art in automatic differentiation (AD) with respect to theory, applications, and tool development. Overall, 53 authors from institutions in 9 countries contributed, demonstrating the worldwide acceptance of AD technology in computational science. Recently it was shown that the problem underlying AD is indeed NP-hard, f- mally proving the inherently challenging nature of this technology. So, most likely, no deterministic “silver bullet” polynomial algorithm can be devised that delivers optimum performance for general codes. In this context, the exploitation of doma- speci?c structural information is a driving issue in advancing practical AD tool and algorithm development. This trend is prominently re?ected in many of the pub- cations in this volume, not only in a better understanding of the interplay of AD and certain mathematical paradigms, but in particular in the use of hierarchical AD approaches that judiciously employ general AD techniques in application-speci?c - gorithmic harnesses. In this context, the understanding of structures such as sparsity of derivatives, or generalizations of this concept like scarcity, plays a critical role, in particular for higher derivative computations.

Automatic Differentiation Applications Theory And Implementations

Author : H. Martin Bücker
ISBN : 9783540284383
Genre : Computers
File Size : 85. 91 MB
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Automatic Differentiation Of Algorithms

Author : Andreas Griewank
ISBN : 089871284X
Genre : Computers
File Size : 28. 82 MB
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Mathematics of Computing -- Numerical Analysis.

Iterative Methods For Sparse Linear Systems

Author : Yousef Saad
ISBN : 9780898715347
Genre : Mathematics
File Size : 64. 91 MB
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Mathematics of Computing -- General.

Iterative Solution Of Nonlinear Equations In Several Variables

Author : J. M. Ortega
ISBN : 9781483276724
Genre : Mathematics
File Size : 70. 35 MB
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Computer Science and Applied Mathematics: Iterative Solution of Nonlinear Equations in Several Variables presents a survey of the basic theoretical results about nonlinear equations in n dimensions and analysis of the major iterative methods for their numerical solution. This book discusses the gradient mappings and minimization, contractions and the continuation property, and degree of a mapping. The general iterative and minimization methods, rates of convergence, and one-step stationary and multistep methods are also elaborated. This text likewise covers the contractions and nonlinear majorants, convergence under partial ordering, and convergence of minimization methods. This publication is a good reference for specialists and readers with an extensive functional analysis background.

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