Hybrid Krylov methods for nonlinear systems of equations
SIAM Journal on Scientific and Statistical Computing
ADIC: an extensible automatic differentiation tool for ANSI-C
Software—Practice & Experience
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
NEOS and Condor: solving optimization problems over the Internet
ACM Transactions on Mathematical Software (TOMS)
A simple automatic derivative evaluation program
Communications of the ACM
Parallel simulation of compressible flow using automatic differentiation and PETSc
Parallel Computing - Special issue on parallel computing in aerospace
Automatic differentiation of algorithms: from simulation to optimization
Automatic differentiation of algorithms: from simulation to optimization
Integrating AD with object-oriented toolkits for high-performance scientific computing
Automatic differentiation of algorithms
Design of new Daspk for Sensitivity Analysis
Design of new Daspk for Sensitivity Analysis
Pseudotransient Continuation and Differential-Algebraic Equations
SIAM Journal on Scientific Computing
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Despite its name, automatic differentiation (AD) is often far from an automatic process. Often one must specify independent and dependent variables, indicate the derivative quantities to be computed, and perhaps even provide information about the structure of the Jacobians or Hessians being computed. When AD is used in conjunction with a toolkit with well-defined interfaces, however, many of these issues can be dealt with automatically. We describe recent research into coupling the ADIC automatic differentiation tool with PETSc, a toolkit for the parallel numerical solution of PDEs. This research leverages the interfaces and objects of PETSc to make the AD process very nearly invisible.