The Art Of Differentiating Computer Programs
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The Art Of Differentiating Computer Programs
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Author : Uwe Naumann
language : en
Publisher: SIAM
Release Date : 2012-01-26
The Art Of Differentiating Computer Programs written by Uwe Naumann and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-26 with Mathematics categories.
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.
Automatic Differentiation In Matlab Using Admat With Applications
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Author : Thomas F. Coleman
language : en
Publisher: SIAM
Release Date : 2016-06-20
Automatic Differentiation In Matlab Using Admat With Applications written by Thomas F. Coleman and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-20 with Science categories.
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.?
Recent Advances In Algorithmic Differentiation
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Author : Shaun Forth
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-30
Recent Advances In Algorithmic Differentiation written by Shaun Forth and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-30 with Mathematics categories.
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.
A Software Repository For Orthogonal Polynomials
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Author : Walter Gautschi
language : en
Publisher: SIAM
Release Date : 2018-03-20
A Software Repository For Orthogonal Polynomials written by Walter Gautschi and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-20 with Science categories.
A Software Repository for Orthogonal Polynomials is the first book that provides graphs and references to online datasets that enable the generation of a large number of orthogonal polynomials with classical, quasi-classical, and nonclassical weight functions. Useful numerical tables are also included. The book will be of interest to scientists, engineers, applied mathematicians, and statisticians.????
A Software Repository For Gaussian Quadratures And Christoffel Functions
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Author : Walter Gautschi
language : en
Publisher: SIAM
Release Date : 2020-10-30
A Software Repository For Gaussian Quadratures And Christoffel Functions written by Walter Gautschi and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-30 with Mathematics categories.
This companion piece to the author’s 2018 book, A Software Repository for Orthogonal Polynomials, focuses on Gaussian quadrature and the related Christoffel function. The book makes Gauss quadrature rules of any order easily accessible for a large variety of weight functions and for arbitrary precision. It also documents and illustrates known as well as original approximations for Gauss quadrature weights and Christoffel functions. The repository contains 60+ datasets, each dealing with a particular weight function. Included are classical, quasi-classical, and, most of all, nonclassical weight functions and associated orthogonal polynomials. Scientists, engineers, applied mathematicians, and statisticians will find the book of interest.
Principles Of Data Assimilation
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Author : Seon Ki Park
language : en
Publisher: Cambridge University Press
Release Date : 2022-09-29
Principles Of Data Assimilation written by Seon Ki Park and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-29 with Science categories.
Data assimilation is theoretically founded on probability, statistics, control theory, information theory, linear algebra, and functional analysis. At the same time, data assimilation is a very practical subject, given its goal of estimating the posterior probability density function in realistic high-dimensional applications. This puts data assimilation at the intersection between the contrasting requirements of theory and practice. Based on over twenty years of teaching courses in data assimilation, Principles of Data Assimilation introduces a unique perspective that is firmly based on mathematical theories, but also acknowledges practical limitations of the theory. With the inclusion of numerous examples and practical case studies throughout, this new perspective will help students and researchers to competently interpret data assimilation results and to identify critical challenges of developing data assimilation algorithms. The benefit of information theory also introduces new pathways for further development, understanding, and improvement of data assimilation methods.
High Performance Computing In Finance
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Author : M. A. H. Dempster
language : en
Publisher: CRC Press
Release Date : 2018-02-21
High Performance Computing In Finance written by M. A. H. Dempster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Computers categories.
High-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computing– that can be used without much expertise and expense – to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Wave’s quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems.
Introduction To High Performance Scientific Computing
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Author : David L. Chopp
language : en
Publisher: SIAM
Release Date : 2019-03-01
Introduction To High Performance Scientific Computing written by David L. Chopp and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-01 with Mathematics categories.
Based on a course developed by the author, Introduction to High Performance Scientific Computing introduces methods for adding parallelism to numerical methods for solving differential equations. It contains exercises and programming projects that facilitate learning as well as examples and discussions based on the C programming language, with additional comments for those already familiar with C++. The text provides an overview of concepts and algorithmic techniques for modern scientific computing and is divided into six self-contained parts that can be assembled in any order to create an introductory course using available computer hardware. Part I introduces the C programming language for those not already familiar with programming in a compiled language. Part II describes parallelism on shared memory architectures using OpenMP. Part III details parallelism on computer clusters using MPI for coordinating a computation. Part IV demonstrates the use of graphical programming units (GPUs) to solve problems using the CUDA language for NVIDIA graphics cards. Part V addresses programming on GPUs for non-NVIDIA graphics cards using the OpenCL framework. Finally, Part VI contains a brief discussion of numerical methods and applications, giving the reader an opportunity to test the methods on typical computing problems.
Computational Science Iccs 2023
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Author : Jiří Mikyška
language : en
Publisher: Springer Nature
Release Date : 2023-06-27
Computational Science Iccs 2023 written by Jiří Mikyška and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-27 with Computers categories.
The five-volume set LNCS 14073-14077 constitutes the proceedings of the 23rd International Conference on Computational Science, ICCS 2023, held in Prague, Czech Republic, during July 3-5, 2023. The total of 188 full papers and 94 short papers presented in this book set were carefully reviewed and selected from 530 submissions. 54 full and 37 short papers were accepted to the main track; 134 full and 57 short papers were accepted to the workshops/thematic tracks. The theme for 2023, "Computation at the Cutting Edge of Science", highlights the role of Computational Science in assisting multidisciplinary research. This conference was a unique event focusing on recent developments in scalable scientific algorithms, advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms, and tools drive new science through efficient application in physical systems, computational andsystems biology, environmental systems, finance, and others.
Algorithmic Differentiation In Finance Explained
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Author : Marc Henrard
language : en
Publisher: Springer
Release Date : 2017-09-04
Algorithmic Differentiation In Finance Explained written by Marc Henrard and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-04 with Business & Economics categories.
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.