Reports, Papers, Posters & Presentations
- dco/c++: Derivative Code by Overloading in C++
- Technical Poster: CVA at Scale with Adjoint Sensitivities – Combining the NAG Library with dco/c++ and Origami
- Technical Poster: High Performance Tape-Free Adjoint AD for C++11
- Technical Report: Adjoint Algorithmic Differentiation Tool Support for Typical Numerical Patterns in Computational Finance
- Technical Report: Adjoint flow solver Tinyflow using dco/c++
- Presentation Slides: Second Order Sensitivities: AAD Construction and Use for CPU and GPU
- Technical Report: Computing Sensitivities of CVA using Adjoint Algorithmic Differentiation
- Technical Poster: From Runtime to Compile Time Adjoints
- Technical Report: Exact First- and Second-Order Greeks by Algorithmic Differentiation
- AD Developer Team Interview: Tool Based Approach to Algorithmic Differentiation of Adjoint Methods
- Technical Report: Adjoint Algorithmic Differentiation of a GPU Accelerated Application
- Technical Report: Toward Adjoint Based Optimization in Computational Finance
- Presentation Slides: Adjoint Methods in Computational Finance
- Techical Report: Why do we need Adjoint routines?
Take the AD Masterclass
NAG is delighted to present an online Algorithmic Differentiation (AD) Masterclass Series. Through a series of Masterclasses NAG shares best AD practice, software engineering issues, optimization techniques and common pitfalls we’ve learned from over a decade in the front lines, applying AD to real-world codes. The series will deepen your AD knowledge and show you how to move beyond toy examples and PoCs.
