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Title: Automatic Differentiation for Gradient Estimators in Simulation
Automatic differentiation (AD) can provide infinitesimal perturbation analysis (IPA) derivative estimates directly from simulation code. These gradient estimators are simple to obtain analytically, at least in principle, but may be tedious to derive and implement in code. AD software tools aim to ease this workload by requiring little more than writing the simulation code. We review considerations when choosing an AD tool for simulation, demonstrate how to apply some specific AD tools to simulation, and provide insightful experiments highlighting the effects of different choices to be made when applying AD in simulation.  more » « less
Award ID(s):
2035086
NSF-PAR ID:
10412975
Author(s) / Creator(s):
; ;
Editor(s):
Feng, B.; Pedrielli, G; Peng, Y.; Shashaani, S.; Song, E.; Corlu, C. G.; Lee, L.H.; Chew, E. P.; Roeder, T.; Lendermann, P.
Date Published:
Journal Name:
Proceedings of the 2022 Winter Simulation Conference
Page Range / eLocation ID:
3134-3145
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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