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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.
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.
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.
The Fourth International Conference on Automatic Di?erentiation was
held
July20-23inChicago,Illinois.Theconferenceincludedaonedayshortcourse,
42 presentations, and a workshop for tool developers. This
gathering of au- matic di?erentiation researchers extended a
sequence that began in Breck- ridge, Colorado, in 1991 and
continued in Santa Fe, New Mexico, in 1996 and Nice, France, in
2000. We invited conference participants and the general - tomatic
di?erentiation community to submit papers to this special
collection.
The28acceptedpapersre?ectthestateoftheartinautomaticdi?erentiation.
The number of automatic di?erentiation tools based on compiler
techn- ogy continues to expand. The papers in this volume discuss
the implem- tation and application of several compiler-based tools
for Fortran, including the venerable ADIFOR, an extended NAGWare
compiler, TAF, and TAPE- NADE. While great progress has been made
toward robust, compiler-based tools for C/C++, most notably in the
form of the ADIC and TAC++ tools, for now operator-overloading
tools such as ADOL-C remain the undisputed champions for
reverse-mode automatic di?erentiation of C++. Tools for - tomatic
di?erentiation of high level languages, including COSY and ADiMat,
continue to grow in importance as the productivity gains o? ered by
high-level programming are recognized.
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