Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
ADIC: an extensible automatic differentiation tool for ANSI-C
Software—Practice & Experience
Advanced compiler design and implementation
Advanced compiler design and implementation
Recipes for adjoint code construction
ACM Transactions on Mathematical Software (TOMS)
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Symbolic bounds analysis of pointers, array indices, and accessed memory regions
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Recomputations in reverse mode AD
Automatic differentiation of algorithms
Automatic differentiation of algorithms
Adifor 2.0: Automatic Differentiation of Fortran 77 Programs
IEEE Computational Science & Engineering
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Towards differentiation-enabled Fortran 95 compiler technology
Proceedings of the 2003 ACM symposium on Applied computing
Optimal interprocedural program optimization: a new framework and its application
Optimal interprocedural program optimization: a new framework and its application
Automatic differentiation for optimum design, applied to sonic boom reduction
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
Journal of Discrete Algorithms
The data-flow equations of checkpointing in reverse automatic differentiation
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Linearity analysis for automatic differentiation
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Hybrid static/dynamic activity analysis
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Tangent-Linear models by augmented LL-Parsers
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
The Tapenade automatic differentiation tool: Principles, model, and specification
ACM Transactions on Mathematical Software (TOMS)
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The automatic generation of adjoints of mathematical models that are implemented as computer programs is receiving increased attention in the scientific and engineering communities. Reverse-mode automatic differentiation is of particular interest for large-scale optimization problems. It allows the computation of gradients at a small constant multiple of the cost for evaluating the objective function itself, independent of the number of input parameters. Source-to-source transformation tools apply simple differentiation rules to generate adjoint codes based on the adjoint version of every statement. In order to guarantee correctness, certain values that are computed and overwritten in the original program must be made available in the adjoint program. For their determination we introduce a static data-flow analysis called ''to be recorded'' analysis. Possible overestimation of this set must be kept minimal to get efficient adjoint codes. This efficiency is essential for the applicability of source-to-source transformation tools to real-world applications.