Recent Advances in Algorithmic Differentiation [electronic resource] / edited by Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther.

Contributor(s): Forth, Shaun [editor.] | Hovland, Paul [editor.] | Phipps, Eric [editor.] | Utke, Jean [editor.] | Walther, Andrea [editor.] | SpringerLink (Online service)
Material type: TextTextSeries: Lecture Notes in Computational Science and Engineering: 87Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Description: XVIII, 362 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642300233Subject(s): Mathematics | Programming languages (Electronic computers) | Numerical analysis | Computer mathematics | Computer software | Mathematical optimization | Mathematics | Computational Mathematics and Numerical Analysis | Computational Science and Engineering | Optimization | Mathematical Software | Numeric Computing | Programming Languages, Compilers, InterpretersAdditional physical formats: Printed edition:: No titleDDC classification: 518 LOC classification: QA71-90Online resources: Click here to access online In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
Item type Current location Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library

Electronic Book@IST

EBook Available
Total holds: 0

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.

There are no comments for this item.

to post a comment.

Powered by Koha