Adjoint Algorithmic Differentiation (AAD) Masterclass 2018

Topics

• Hands-on case studies of application of AAD to Monte Carlo method for SDEs
• The mathematical foundations of AAD
• Second and higher- order AAD and corresponding support offered by dco/c++
• Hands-on case studies of application of AAD to finite difference method for PDEs
• Detection and exploitation of sparsity in Jacobians and Hessians
• Advanced issues in AAD including
  • Symbolic adjoints of implicit functions including linear and nonlinear solvers and optimization methods
  • Methods for reducing the memory requirement of adjoint code
  • AAD on GPUs
  • And exploitation of shared and distributed memory parallelism in AAD