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Main navigation

  • Home
  • Services
    • Algorithmic Differentiation Solutions
      • Algorithmic Differentiation Solutions
      • Algorithmic Differentiation Services
      • Algorithmic Differentiation Software
      • Algorithmic Differentiation Research
    • Mathematical Optimization Consultancy
    • Cloud HPC Migration Service
    • Software Optimization and Code Modernization
    • Algorithm Design & Development
    • Software Porting & Tuning
    • Training Courses
    • Case Studies
  • Software
    • Algorithmic Differentiation Software
    • Mathematical Optimization Software
    • NAG Library
      • NAG Library
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      • NAG Library for C
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      • NAG Library for Python
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      • NAG Library for SMP & Multicore
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      • NAG Fortran Compiler
      • NAG Fortran Builder
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      • Library for .NET
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      • Library for Java versions
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      • Library for the Xeon Phi
      • Toolbox for MATLAB® Versions
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    • TCO Calculator
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    • POP EU Project
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Algorithmic Differentation Repository

NAG are pioneers in providing Agorithmic Differentiation technologies. Learn more about what we do from the technical material in our AD Research Repository.

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?
AD Solutions more information
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