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An Efficient Finite Difference Approximation via a Double Sample-Recycling Approach
May 10, 2024, 4:42 a.m. | Guo Liang, Guangwu Liu, Kun Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: Estimating stochastic gradients is pivotal in fields like service systems within operations research. The classical method for this estimation is the finite difference approximation, which entails generating samples at perturbed inputs. Nonetheless, practical challenges persist in determining the perturbation and obtaining an optimal finite difference estimator in the sense of possessing the smallest mean squared error (MSE). To tackle this problem, we propose a double sample-recycling approach in this paper. Firstly, pilot samples are recycled …
abstract approximation arxiv challenges cs.lg cs.na difference fields inputs math.na math.oc operations pivotal practical recycling research sample samples service stat.me stochastic systems type via
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