Adjoint Algorithmic Differentiation

This course will provide a comprehensive understanding of the mathematical foundations of AAD and its role within financial applications<br />

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Adjoint Algorithmic Differentiation (AAD) Masterclass

London, 20-21 February 2019

View the agenda   Early bird registration

This two-day course, provides a practical introduction to algorithmic differentiation (AD). Attendees will discuss the mathematical foundations for adjoints methods, algorithmic differentiation (AD) as a general computational technique for the efficient calculation of price sensitivities, and the use of AD software as a way to generate the adjoint code. Focus will be placed on its application to Monte Carlo methods for SDEs and finite difference methods for PDEs.

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What Will You Learn?
  • Comprehensive understanding of the mathematical foundations of AAD and its role within financial applications
  • Hands-on case studies of application of AAD to Monte Carlo method for SDEs
  • Hands-on case studies of application of AAD to finite difference method for PDEs
  • Detailed understanding of second and higher-order AAD and of dco/c++ tool support  
  • Understanding of advanced issues in AAD including:
    • methods for reducing the memory requirement of adjoint code
    • symbolic adjoints of implicit functions including linear and nonlinear solvers and optimization methods
    • handling and exploitation of shared and distributed memory parallelism in AAD
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Who Should Attend?

Relevant departments may include but are not limited to:

  • Quantitative Analysis
  • Trading
  • Risk Management
  • Fixed Income
  • Equity / Credit / Commodity / FX Derivatives
  • Portfolio Management
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Course Highlights

After the course participants will have gained knowledge of:

  • Adjoints in Finance I 
  • Writing First-Order Adjoint Code by Hand 
  • Adjoints in Finance II 
  • Introduction to AAD Software Tool dco/c++ 
  • Hands-on First-Order AAD with dco/++ 

Amba Hotel

Strand,
Charing Cross,
London, 
WC2N 5HX

Venue information

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