Stochastic Automatic Differentiation: Efficient Tapeless Implementation of Automatic Differentiation for Monte-Carlo Simulations
Posted: 1 Aug 2017
Date Written: July 27, 2017
In this paper we present an efficient implementation of automatic differentiations of random variables (see https://ssrn.com/abstract=2995695).
Using this implementation can increase the speed of the calculation of the automatic differentiation and reduce the memory requirements.
In some cases this approach may give surprising results: We give examples where the the calculation of all partial derivatives is 10000-times faster compared to the calculation of the value and the memory footprint is the same. A trivial example is the AAD calculation of a sum of random variables. While this is an operator on a random variable (i.e., a vector of thousands of Monte-Carlo samples), the partial derivatives are just scalars.
Keywords: Automatic Differentiation, Adjoint Automatic Differentiation Monte Carlo Simulation, Object Oriented Implementation
JEL Classification: C15, G13
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