Biased gradient estimators in simulation optimization

Biased gradient estimators in simulation optimization. Eckman, D. J. & Henderson, S. G. In Bae, K., Feng, B., Kim, S., Lazarova-Molnar, S., Zheng, Z., Roeder, T., & Thiesing, R., editors, Proceedings of the 2020 Winter Simulation Conference, pages 2935–2946, Piscataway NJ, 2020. IEEE.

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Within the simulation community, the prevailing wisdom seems to be that when solving a simulation optimization problem, biased gradient estimators should not be used to guide a local-search algorithm. On the contrary, we argue that for certain problems, biased gradient estimators may still provide useful directional information. We focus on the infinitesimal perturbation analysis (IPA) gradient estimator, which is biased when an interchange of differentiation and expectation fails. Although a local-search algorithm guided by biased gradient estimators will likely not converge to a local optimal solution, it might be expected to reach a neighborhood of one. We test such a gradient-based search on an ambulance base location problem, demonstrating its effectiveness in a non-trivial example, and present some supporting theoretical results.

@inproceedings{eckhen20,
	abstract = {Within the simulation community, the prevailing wisdom seems to be that when solving a simulation optimization problem, biased gradient estimators should not be used to guide a local-search algorithm.
On the contrary, we argue that for certain problems, biased gradient estimators may still provide useful directional information.
We focus on the infinitesimal perturbation analysis (IPA) gradient estimator, which is  biased when an interchange of differentiation and expectation fails.
Although a local-search algorithm guided by biased gradient estimators will likely not converge to a local optimal solution, it might be expected to reach a neighborhood of one. We test such a gradient-based search on an ambulance base location problem, demonstrating its effectiveness in a non-trivial example, and present some supporting theoretical results.},
	address = {Piscataway {NJ}},
	author = {David J. Eckman and Shane G. Henderson},
	booktitle = {Proceedings of the 2020 Winter Simulation Conference},
	date-added = {2020-04-23 09:35:21 -0400},
	date-modified = {2021-07-08 16:51:27 -0400},
	editor = {K.-H. Bae and B. Feng and S. Kim and S. {Lazarova-Molnar} and Z. Zheng and T. Roeder and R. Thiesing},
	organization = {IEEE},
	pages = {2935--2946},
	title = {Biased gradient estimators in simulation optimization},
	url_paper = {https://informs-sim.org/wsc20papers/299.pdf},
	year = {2020}}

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