@incollection{foreckhen22,
	abstract = {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.},
	author = {Matthew T. Ford and David J. Eckman and Shane G. Henderson},
	booktitle = {Proceedings of the 2022 Winter Simulation Conference},
	date-added = {2022-04-30 09:03:13 -0400},
	date-modified = {2024-08-12 14:41:36 -0400},
	editor = {B. Feng and G. Pedrielli and Y. Peng and S. Shashaani and E. Song and C.G. Corlu and L.H. Lee and P. Lendermann},
	pages = {3134-3145},
	publisher = {IEEE},
	title = {Automatic differentiation for gradient estimators in simulation},
	url_paper = {https://www.informs-sim.org/wsc22papers/320.pdf},
	year = {2022}}

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