000 03597nam a22005895i 4500
001 978-3-642-30023-3
003 DE-He213
005 20140220083317.0
007 cr nn 008mamaa
008 120730s2012 gw | s |||| 0|eng d
020 _a9783642300233
_9978-3-642-30023-3
024 7 _a10.1007/978-3-642-30023-3
_2doi
050 4 _aQA71-90
072 7 _aPBKS
_2bicssc
072 7 _aMAT006000
_2bisacsh
082 0 4 _a518
_223
082 0 4 _a518
_223
100 1 _aForth, Shaun.
_eeditor.
245 1 0 _aRecent Advances in Algorithmic Differentiation
_h[electronic resource] /
_cedited by Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXVII, 361 p. 89 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computational Science and Engineering,
_x1439-7358 ;
_v87
520 _aThe 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.
650 0 _aMathematics.
650 0 _aComputer science.
650 0 _aElectronic data processing.
650 0 _aComputer science
_xMathematics.
650 0 _aComputer software.
650 0 _aMathematical optimization.
650 1 4 _aMathematics.
650 2 4 _aComputational Mathematics and Numerical Analysis.
650 2 4 _aComputational Science and Engineering.
650 2 4 _aOptimization.
650 2 4 _aMathematical Software.
650 2 4 _aNumeric Computing.
650 2 4 _aProgramming Languages, Compilers, Interpreters.
700 1 _aHovland, Paul.
_eeditor.
700 1 _aPhipps, Eric.
_eeditor.
700 1 _aUtke, Jean.
_eeditor.
700 1 _aWalther, Andrea.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642300226
830 0 _aLecture Notes in Computational Science and Engineering,
_x1439-7358 ;
_v87
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-30023-3
912 _aZDB-2-SMA
999 _c103118
_d103118