Authors

Matthew Supernaw

Description

The Analytics Template Library (ATL) is a scientific computing library with an emphasis on gradient based optimization. ATL leverages the power of template metaprogramming for flexibility, extensibility, and speed. This guide is intended to give the user a basic understanding of how to develop programs in ATL. The information in this document is intended for anyone interested in scientific computing in C++ and it is expected that the reader will have a basic understanding of the C++ programming language, as well as scientific computing.

References

• Andreas Griewank, Andrea Walther [Introduction to Automatic Differentiation]. PAMM Proc. Appl. Math. Mech. 2, 45?49 (2003).
• Andreas Griewank [On Automatic Differentiation]. Center for Research on Parallel Computation, 1989.
• Robert Mansel Gower, Margarida P. Mello Hessian Matrices via Automatic Differentiation. Institute of Mathematics, Statistics and Scientific Computing,State University of Campinas, September 29, 2010.
• Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., and Sibert, J. 2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. 27:233-249.
• Kasper Kristensen and Anders Nielsen and Casper W. Berg and Hans Skaug and Bradley M. Bell, 2016, TMB: Automatic Differentiation and Laplace Approximation, Journal of Statistical Software, 70:1-21

Code Repository Badges

Keywords

• Maximum Likelihood Estimation
• Model Builder
• Gradient Solver

Site Index