Adjoint Algorithmic
Differentiation (AAD) Masterclass
London, 21-22 March 2018

In this two-day course, attendees 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.

Course Highlights:

  • Receive a practical, hands-on understanding of the effective application and use of AAD 
  • Understand how AAD methods can give an unlimited number of Greeks at a cost which is a small multiple of the original pricing cost 
  • Learn how adjoint code can be written by hand in C/C++
  • Write your own adjoint SDE/Monte Carlo or PDE/finite difference code 
  • Learn how AD software tools can aid the development and maintenance of adjoint code
  • Apply AAD software tool dco/c++ to your own SDE/Monte Carlo or PDE/finite difference code; get access to a 3-months trail license for dco/c++

Learning Outcomes:

  • Comprehensive understanding of the mathematical foundations of AAD
  • 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
  • Learn about second and higher-order AAD and corresponding support offered by dco/c++ 
  • Learn about 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 
    • AAD on GPUs 
  • Detection and exploitation of sparsity in Jacobians and Hessians

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Course Tutor:

  • Uwe Naumann, Professor of Computer Science, RWTH Aachen University

Professor Naumann has been providing AAD solutions and consultancy to various tier-1 investment banks for several years. He is the author of the popular textbook "The Art of Differentiating Computer Programs. An Introduction to Algorithmic Differentiation" published by SIAM in 2012.

Who Should Attend?

The training will be beneficial to those working in the following areas:

  • Financial Analysts/Engineers
  • Quantitative Analysts
  • Traders
  • Risk Managers
  • Risk Management
  • Quantitative Analysis/Research
  • Fixed Income
  • Equity Derivatives
  • Credit Derivatives
  • Hybrids/Commodity Derivatives/FX Derivatives
  • Portfolio Management
  • Quantitative Investment Strategies
  • Managing Directors
  • Partners
  • CEOs/ Presidents
  • CROs

Pricing and Registration >>
Course Agenda >>

Risk Training Calendar Q1 2018
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