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

搖曳的微光

aggressor

https://doi.org/10.1007/978-3-319-49941-3 practical. For general problems, automatic differentiation is likely to be the most convenient means of exploiting second derivatives. We delineate a role for automatic differentiation in matrix-free optimization formulations involving Newton’s method, in which little more storage is required than that for the analysis code alone.

mortuary