Advances in Automatic Differentiation for the Java Programming Language PDF Download

Author: Emil Ioan Sluşanschi
Publisher:
ISBN: 9789735969080
Category :
Languages : en
Pages : 123

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Author: Shaun Forth
Publisher: Springer Science & Business Media
ISBN: 3642300235
Category : Mathematics
Languages : en
Pages : 356

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Book Description
The proceedings represent the state of knowledge in the area of algorithmic differentiation (AD). The 31 contributed papers presented at the AD2012 conference cover the application of AD to many areas in science and engineering as well as aspects of AD theory and its implementation in tools. For all papers the referees, selected from the program committee and the greater community, as well as the editors have emphasized accessibility of the presented ideas also to non-AD experts. In the AD tools arena new implementations are introduced covering, for example, Java and graphical modeling environments or join the set of existing tools for Fortran. New developments in AD algorithms target the efficiency of matrix-operation derivatives, detection and exploitation of sparsity, partial separability, the treatment of nonsmooth functions, and other high-level mathematical aspects of the numerical computations to be differentiated. Applications stem from the Earth sciences, nuclear engineering, fluid dynamics, and chemistry, to name just a few. In many cases the applications in a given area of science or engineering share characteristics that require specific approaches to enable AD capabilities or provide an opportunity for efficiency gains in the derivative computation. The description of these characteristics and of the techniques for successfully using AD should make the proceedings a valuable source of information for users of AD tools.

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.

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.

Author: Paul Dennis Hovland
Publisher:
ISBN:
Category : Coding theory
Languages : en
Pages : 124

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Book Description
Abstract: "There are many areas of computational science in which it is necessary or desirable to compute derivatives. Automatic differentiation (AD) tools such as ADIFOR and ADIC have proven very useful for developing derivative code for programs written in Fortran and C. However, many scientific applications are written for or ported to parallel platforms to maximize performance. We have developed tools and techniques for applying AD to parallel programs, paying special attention to message- passing parallel programs. We list several potential problems that arise in differentiating parallel programs and present solutions for each of them. Some of the issues concern the correctness of the generated code, whereas others concern performance. While many of the issues have analogues in sequential programs, the solution is often quite different. In addition, some new concerns arise that are unique to parallel programs. We also describe how the tools and techniques developed to enable AD of parallel programs were applied to a variety of applications, ranging from a simple test problem to a parallel molecular dynamics application. The results confirm the need for and efficacy of several techniques. They also verify the prediction that the program generated by AD will generally demonstrate better speedup and scalability than the original program. We conclude with some brief remarks on how AD can be applied to other types of parallel programs and a description of how this work relates to other research in the areas of AD and scientific computing."

Author: George Corliss
Publisher: Springer Science & Business Media
ISBN: 1461300754
Category : Computers
Languages : en
Pages : 431

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Book Description
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.

Author: Bertrand Braunschweig
Publisher: Elsevier
ISBN: 0080541364
Category : Computers
Languages : en
Pages : 713

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Book Description
The idea of editing a book on modern software architectures and tools for CAPE (Computer Aided Process Engineering) came about when the editors of this volume realized that existing titles relating to CAPE did not include references to the design and development of CAPE software. Scientific software is needed to solve CAPE related problems by industry/academia for research and development, for education and training and much more. There are increasing demands for CAPE software to be versatile, flexible, efficient, and reliable. This means that the role of software architecture is also gaining increasing importance. Software architecture needs to reconcile the objectives of the software; the framework defined by the CAPE methods; the computational algorithms; and the user needs and tools (other software) that help to develop the CAPE software. The object of this book is to bring to the reader, the software side of the story with respect to computer aided process engineering.

Author: Songchen Tan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Differentiable programming is a new paradigm for modeling and optimization in many fields of science and engineering, and automatic differentiation (AD) algorithms are at the heart of differentiable programming. Existing methods to achieve higher-order AD often suffer from one or more of the following problems: (1) exponential scaling with respect to order due to nesting first-order AD; (2) ad-hoc handwritten higher-order rules which are hard to maintain and do not utilize existing first-order AD infrastructures; (3) inefficient data representation and manipulation that causes significant overhead at lowered-order when compared to nesting highly-optimized first-order AD libraries. By combining advanced techniques in computational science, i.e., aggressive type specializing, metaprogramming, and symbolic computing, we introduce a new implementation of Taylor mode automatic differentiation in Julia that addresses these problems. The new implementation shows that it is possible to achieve higher-order AD with minimal overhead and without sacrificing the performance of lower-order AD and obtain significant speedup in real-world scenarios over the existing Julia AD library. In addition, this implementation automatically generates higher-order AD rules from first-order AD rules, which is a step towards a general framework for higher-order AD.

Author: Gershon Kedem
Publisher:
ISBN:
Category :
Languages : en
Pages : 55

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Book Description
A method for the automatic differentiation of computer functions (subroutines) written in a high level language is discussed. A theory is developed to show that most functions that arise in applications can be differentiated automatically. It is shown how one can take FORTRAN function (Subroutine) and, with the aid of a precompiler, obtain a FORTRAN subroutine that computes the original function and its desired derivatives. Implementation of two types of differentiation is described: in terms of (1) automatic Taylor series expansion of FORTRAN programs, and (2) automatic Gradient calculation of FORTRAN functions.

Author: Andreas Kowarz
Publisher:
ISBN:
Category :
Languages : en
Pages : 107

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