{"id":374,"date":"2023-04-25T12:49:19","date_gmt":"2023-04-25T12:49:19","guid":{"rendered":"https:\/\/nag.com\/?page_id=374"},"modified":"2025-08-18T13:01:50","modified_gmt":"2025-08-18T13:01:50","slug":"automatic-differentiation","status":"publish","type":"page","link":"https:\/\/nag.com\/automatic-differentiation\/","title":{"rendered":"Automatic Differentiation"},"content":{"rendered":"\n
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n<\/i><\/span>AG Automatic Differentiation dco\/c++<\/h1>\n<\/div>\n

n<\/i><\/span>AG continues to innovate in Automatic Differentiation with our pioneering solution for numerical sensitivity analysis.<\/p>\n<\/div>\n\n Contact our experts <\/i><\/a>\n <\/div>\n

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Enhanced Intelligence, Expert Support<\/h2>\n \n

n<\/i><\/span>AG dco\/c++ calculates mathematical derivatives with machine accuracy, up to 36,000x faster than alternative methods. Built to vastly enhance decision-making by improving computational runtime and memory use for numerical sensitivity analysis.<\/p>\n

Commercially supported and continuously updated by n<\/i><\/span>AG experts, dco\/c++ delivers enhanced data and opportunity across engineering, science, and banking.<\/p>\n<\/div>\n\n \n Buy now <\/i><\/a> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n\n\n

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n<\/i><\/span>AG Automatic Differentiation dco\/c++<\/span><\/strong><\/span><\/p>\n

Feature Rich,
Highly Efficient
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n<\/i><\/span>AG 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.<\/p>\n

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dco\/c++ has been specifically designed and developed for continuous computational models. n<\/i><\/span>AG dco\/c++ is the most feature-rich and efficient automatic differentiation solution available.<\/p>\n <\/div>\n\n <\/div>\n

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n<\/i><\/span>AG Automatic Differentiation dco\/c++<\/span><\/strong><\/span><\/p>\n

Case Studies<\/span><\/h2>\n \n
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DekaBank Improves Modelling Accuracy<\/span><\/h4>\n<\/div>\n \n

DekaBank wanted better risk management, more accurate pricing and to support the bank\u2019s<\/p>\n<\/div>\n \n\n\n <\/div>\n\n \n <\/a>\n <\/div>\n

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Team Sonnenwagen Optimize Solar Racing Car<\/span><\/h4>\n<\/div>\n \n

Team Sonnenwagen is a group of students from the Aachen Universities. They participated<\/p>\n<\/div>\n \n\n\n <\/div>\n\n \n <\/a>\n <\/div>\n

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Reverse Mode AD = Faster Risk Management, Better Pricing, and Cheaper Computer<\/span><\/h4>\n<\/div>\n \n

How can banks best manage CVA risk to protect revenue,
remain agile under pressure<\/p>\n<\/div>\n \n\n\n <\/div>\n\n \n <\/a>\n <\/div>\n \n \n <\/div>\n \n \n \n

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