Titlebook: Automatic Differentiation of Algorithms; From Simulation to O George Corliss,Christèle Faure,Uwe Naumann Book 2002 Springer Science+Busines

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Differentiation Methods for Industrial Strength Problemsputer models have to be developed more quickly. The correct description and implementation of the interaction between different components of the entire simulation model as well as the nonlinear behaviour of these components lead in many cases to the need for derivative information. In this chapter,

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Automatic Differentiation Tools in Optimization Softwared that have proved useful in the installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of the gradient and Hessian matrix for partially separable functions and shows that the gradient and Hessian matrix can be computed with guaranteed bounds in time and memo

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Using Automatic Differentiation for Second-Order Matrix-free Methods in PDE-constrained Optimizationg second-derivative information is not. Both assumptions need revision for the application of optimization to systems constrained by partial differential equations, in the contemporary limit of millions of state variables and in the parallel setting. Large-scale PDE solvers are complex pieces of sof

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Present and Future Scientific Computation Environmentsion of real-world applications. In this chapter we discuss some of the experiences of NAG Ltd. in several European projects aiming towards the development of such tools. It will also note some of the encouraging developments, particularly in the area of interface standards that might make PSE constr

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A Case Study of Computational Differentiation Applied to Neutron Scattering Automatic differentiation enables, in a completely mechanical fashion, algorithmic changes by switching from a quasi-Newton method, where first order derivatives are approximated by finite differences, to a modified Gauss-Newton method using exact first order derivatives. Compared to the original c

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