Automatic Differentiation: Applications, Theory, and Implementations

Automatic Differentiation: Applications, Theory, and Implementations PDF Author: H. Martin Bücker
Publisher: Springer Science & Business Media
ISBN: 3540284389
Category : Computers
Languages : en
Pages : 370

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Book Description
Covers the state of the art in automatic differentiation theory and practice. Intended for computational scientists and engineers, this book aims to provide insight into effective strategies for using automatic differentiation for design optimization, sensitivity analysis, and uncertainty quantification.

Automatic Differentiation: Applications, Theory, and Implementations

Automatic Differentiation: Applications, Theory, and Implementations PDF Author: H. Martin Bücker
Publisher: Springer Science & Business Media
ISBN: 3540284389
Category : Computers
Languages : en
Pages : 370

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Book Description
Covers the state of the art in automatic differentiation theory and practice. Intended for computational scientists and engineers, this book aims to provide insight into effective strategies for using automatic differentiation for design optimization, sensitivity analysis, and uncertainty quantification.

Automatic Differentiation of Algorithms

Automatic Differentiation of Algorithms PDF Author: Andreas Griewank
Publisher: Society for Industrial & Applied
ISBN: 9780898712841
Category : Computers
Languages : en
Pages : 353

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Book Description
Mathematics of Computing -- Numerical Analysis.

Advances in Automatic Differentiation

Advances in Automatic Differentiation PDF Author: Christian H. Bischof
Publisher: Springer Science & Business Media
ISBN: 3540689427
Category : Computers
Languages : en
Pages : 366

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Book Description
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.

Evaluating Derivatives

Evaluating Derivatives PDF Author: Andreas Griewank
Publisher: SIAM
ISBN: 0898716594
Category : Mathematics
Languages : en
Pages : 448

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Book Description
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.

Automatic Differentiation in MATLAB Using ADMAT with Applications

Automatic Differentiation in MATLAB Using ADMAT with Applications PDF Author: Thomas F. Coleman
Publisher: SIAM
ISBN: 1611974356
Category : Science
Languages : en
Pages : 114

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Book Description
The calculation of partial derivatives is a fundamental need in scientific computing. Automatic differentiation (AD) can be applied straightforwardly to obtain all necessary partial derivatives (usually first and, possibly, second derivatives) regardless of a code?s complexity. However, the space and time efficiency of AD can be dramatically improved?sometimes transforming a problem from intractable to highly feasible?if inherent problem structure is used to apply AD in a judicious manner. Automatic Differentiation in MATLAB using ADMAT with Applications discusses the efficient use of AD to solve real problems, especially multidimensional zero-finding and optimization, in the MATLAB environment. This book is concerned with the determination of the first and second derivatives in the context of solving scientific computing problems with an emphasis on optimization and solutions to nonlinear systems. The authors focus on the application rather than the implementation of AD, solve real nonlinear problems with high performance by exploiting the problem structure in the application of AD, and provide many easy to understand applications, examples, and MATLAB templates.

The Art of Differentiating Computer Programs

The Art of Differentiating Computer Programs PDF Author: Uwe Naumann
Publisher: SIAM
ISBN: 161197206X
Category : Mathematics
Languages : en
Pages : 348

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Book Description
In this entry-level book on algorithmic (also known as automatic) differentiation (AD) the author covers the mathematical underpinnings as well as applications to real-world numerical simulation programs. Readers will find many examples and exercises, including hints to solutions. A supplementary website contains software sources, additional exercises, useful links and errata.

Theory and Application of Graphs

Theory and Application of Graphs PDF Author: Junming Xu
Publisher: Springer Science & Business Media
ISBN: 9781402075407
Category : Mathematics
Languages : en
Pages : 346

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Book Description
In the spectrum of mathematics, graph theory which studies a mathe matical structure on a set of elements with a binary relation, as a recognized discipline, is a relative newcomer. In recent three decades the exciting and rapidly growing area of the subject abounds with new mathematical devel opments and significant applications to real-world problems. More and more colleges and universities have made it a required course for the senior or the beginning postgraduate students who are majoring in mathematics, computer science, electronics, scientific management and others. This book provides an introduction to graph theory for these students. The richness of theory and the wideness of applications make it impossi ble to include all topics in graph theory in a textbook for one semester. All materials presented in this book, however, I believe, are the most classical, fundamental, interesting and important. The method we deal with the mate rials is to particularly lay stress on digraphs, regarding undirected graphs as their special cases. My own experience from teaching out of the subject more than ten years at University of Science and Technology of China (USTC) shows that this treatment makes hardly the course di:fficult, but much more accords with the essence and the development trend of the subject.

A Mathematical Theory of Design: Foundations, Algorithms and Applications

A Mathematical Theory of Design: Foundations, Algorithms and Applications PDF Author: D. Braha
Publisher: Springer Science & Business Media
ISBN: 1475728727
Category : Technology & Engineering
Languages : en
Pages : 684

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Book Description
Formal Design Theory (PDT) is a mathematical theory of design. The main goal of PDT is to develop a domain independent core model of the design process. The book focuses the reader's attention on the process by which ideas originate and are developed into workable products. In developing PDT, we have been striving toward what has been expressed by the distinguished scholar Simon (1969): that "the science of design is possible and some day we will be able to talk in terms of well-established theories and practices. " The book is divided into five interrelated parts. The conceptual approach is presented first (Part I); followed by the theoretical foundations of PDT (Part II), and from which the algorithmic and pragmatic implications are deduced (Part III). Finally, detailed case-studies illustrate the theory and the methods of the design process (Part IV), and additional practical considerations are evaluated (Part V). The generic nature of the concepts, theory and methods are validated by examples from a variety of disciplines. FDT explores issues such as: algebraic representation of design artifacts, idealized design process cycle, and computational analysis and measurement of design process complexity and quality. FDT's axioms convey the assumptions of the theory about the nature of artifacts, and potential modifications of the artifacts in achieving desired goals or functionality. By being able to state these axioms explicitly, it is possible to derive theorems and corollaries, as well as to develop specific analytical and constructive methodologies.

Distribution Theory of Runs and Patterns and Its Applications

Distribution Theory of Runs and Patterns and Its Applications PDF Author: James C. Fu
Publisher: World Scientific
ISBN: 9810245874
Category : Mathematics
Languages : en
Pages : 174

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Book Description
A rigorous, comprehensive introduction to the finite Markov chain imbedding technique for studying the distributions of runs and patterns from a unified and intuitive viewpoint, away from the lines of traditional combinatorics.

Algorithmic Differentiation in Finance Explained

Algorithmic Differentiation in Finance Explained PDF Author: Marc Henrard
Publisher: Springer
ISBN: 3319539795
Category : Business & Economics
Languages : en
Pages : 112

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Book Description
This book provides the first practical guide to the function and implementation of algorithmic differentiation in finance. Written in a highly accessible way, Algorithmic Differentiation Explained will take readers through all the major applications of AD in the derivatives setting with a focus on implementation. Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and data assimilation for many years. Over the last decade, it has been increasingly (and successfully) applied to financial risk management, where it provides an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Calculating derivatives exposure across a portfolio is no simple task. It requires many complex calculations and a large amount of computer power, which in prohibitively expensive and can be time consuming. Algorithmic differentiation techniques can be very successfully in computing Greeks and sensitivities of a portfolio with machine precision. Written by a leading practitioner who works and programmes AD, it offers a practical analysis of all the major applications of AD in the derivatives setting and guides the reader towards implementation. Open source code of the examples is provided with the book, with which readers can experiment and perform their own test scenarios without writing the related code themselves.