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The Tapenade automatic differentiation tool: Principles, model, and specification
L Hascoet, V Pascual - ACM Transactions on Mathematical Software …, 2013 - dl.acm.org
Tapenade is an Automatic Differentiation (AD) tool which, given a Fortran or C code that
computes a function, creates a new code that computes its tangent or adjoint derivatives …
computes a function, creates a new code that computes its tangent or adjoint derivatives …
[LLIBRE][B] Evaluating derivatives: principles and techniques of algorithmic differentiation
A Griewank, A Walther - 2008 - SIAM
The advent of high-speed computers and sophisticated software tools has made the
computation of derivatives for functions defined by evaluation programs both easier and …
computation of derivatives for functions defined by evaluation programs both easier and …
OpenAD/F: A modular open-source tool for automatic differentiation of Fortran codes
The Open/ADF tool allows the evaluation of derivatives of functions defined by a Fortran
program. The derivative evaluation is performed by a Fortran code resulting from the …
program. The derivative evaluation is performed by a Fortran code resulting from the …
A bibliography of automatic differentiation
A bibliography of automatic differentiation Page 325 A Bibliography of Automatic Differentiation
H. Martin Bücker1 and George F. Corliss2 1 Institute for Scientific Computing, RWTH Aachen …
H. Martin Bücker1 and George F. Corliss2 1 Institute for Scientific Computing, RWTH Aachen …
Computing adjoints with the NAGWare Fortran 95 compiler
U Naumann, J Riehme - Automatic Differentiation: Applications, Theory …, 2006 - Springer
We present a new experimental version of the differentiation-enabled NAGWare Fortran 95
compiler (from now on referred to as “the AD compiler”) that provides support for the …
compiler (from now on referred to as “the AD compiler”) that provides support for the …
Higher-order discrete adjoint ODE solver in C++ for dynamic optimization
Parametric ordinary differential equations (ODE) arise in many engineering applications. We
consider ODE solutions to be embedded in an overall objective function which is to be …
consider ODE solutions to be embedded in an overall objective function which is to be …
ADiJaC--Automatic differentiation of Java classfiles
EI Sluşanschi, V Dumitrel - ACM Transactions on Mathematical Software …, 2016 - dl.acm.org
This work presents the current design and implementation of ADiJaC, an automatic
differentiation tool for Java classfiles. ADiJaC uses source transformation to generate …
differentiation tool for Java classfiles. ADiJaC uses source transformation to generate …
Optimal checkpointing for time-step** procedures in ADOL-C
A Kowarz, A Walther - International Conference on Computational Science, 2006 - Springer
Using the basic reverse mode of automatic differentiation, the memory needed for the
computation of discrete adjoints is proportional to the number of operations performed. This …
computation of discrete adjoints is proportional to the number of operations performed. This …
[LLIBRE][B] Toward Adjoint OpenMP
Shared-memory multiprocessing is becoming increasingly important in high-performance
scientific computing. Algorithmic differentiation provides accurate derivative values and …
scientific computing. Algorithmic differentiation provides accurate derivative values and …
[PDF][PDF] OpenAD/F: user manual
A general introduction to the aims of the OpenAD/F tool and the underlying principles was
given in an ACM TOMS paper [46]. Because of the ongoing development of the tool a …
given in an ACM TOMS paper [46]. Because of the ongoing development of the tool a …