NAG Automatic Differentiation dco/c++
NAG dco/c++ is the most powerful and widely used Automatic Differentiation technology for C++ on the market. It is a library of advanced mathematical and computer science algorithms built for calculating directional and adjoint derivatives of differentiable programs.
dco/c++ has been specifically designed and developed for continuous computational models. NAG dco/c++ is the most feature-rich and efficient automatic differentiation solution available.
DekaBank wanted better risk management, more accurate pricing and to support the bank’s
Team Sonnenwagen is a group of students from the Aachen Universities. They participated
dco/c++ is backed by science. NAG ensures that our product and your investments are future-proofed through collaboration, innovation, and investment.
NAG dco/c++ is a feature-rich library of advanced mathematical and computer science algorithms. It is built to vastly improve numerical sensitivity analysis and decision-making through enhanced intelligence.
dco/c++ calculates first and higher-order tangent and adjoint derivatives up to 36,000x faster than alternative methods, and with machine accuracy. It is a robust, commercially backed, supported, continuously maintained and updated product. Ultimately, dco/c++ delivers confidence, peace of mind and a competitive edge to any business using continuous computational models.
An expert assessment of the impact that AD would bring to your modeling, and business function, giving you the confidence to move forward with an AD investment strategy.
NAG AD experts collaborate with you to integrate an AD solution into your models. We provide assistance in evaluating the effectiveness of the solution, supporting you in demonstrating the impact of your investment.
During implementation, NAG’s AD Consultant Developers will work with you on:
With NAG thee are no delays to projects and you can have maximum confidence in your operations. Our experts are always on hand to make sure you are safe in the knowledge that there will always be someone ready to help you.
NAG will provide troubleshooting telephone, email, or video call support on API and features, triaging bugs and fixing them, and “general advice” on AD to a level which ensures the customer gets the best out of the dco/c++.
Take advantage of the NAG world-class developers and their expert knowledge to train your teams to work with dco/c++ in the most effective way for your business. Our comprehensive training ensures best practices are used and dco/c++ is implemented in the most efficient way and is designed with your specific needs in mind.
Easy integration and high performance.
A hybrid technology that combines the efficiency of source transformation with the flexibility and ease of use of an operator overloading tool.
Combine dco/c++ with hand-written adjoints, symbolic adjoints (e.g., implicit function theorem), or any other way of computing sensitivities of embedded algorithms.
Cheaply detect zero entries in your derivative tensor.
Preaccumulating local Jacobians reduces tape size and tape interpretation time.
STL, BLAS, Boost, Eigen, …. and more.