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Estimating Derivatives of Noisy Simulations

Jorge Moré (more***at***mcs.anl.gov)
Stefan Wild (wild***at***mcs.anl.gov)

Abstract: We employ recent work on computational noise to obtain near-optimal finite difference estimates of the derivatives of a noisy function. Our analysis employs a stochastic model of the noise without assuming a specific form of distribution. We use this model to derive theoretical bounds for the errors in the difference estimates and obtain an easily computable difference parameter that is provably near-optimal. Numerical results closely resemble the theory and show that we obtain accurate derivative estimates even when the noisy function is deterministic.

Keywords: Computational Noise, Finite Difference Derivatives, Noisy Simulations

Category 1: Other Topics (Other )

Category 2: Nonlinear Optimization (Other )

Category 3: Optimization Software and Modeling Systems (Other )

Citation: To appear in ACM Transactions on Mathematical Software, Vol 38, No 3. Formerly: Argonne National Laboratory Mathematics and Computer Science Division Preprint ANL/MCS-P1785-0810.

Download: [PDF]

Entry Submitted: 11/02/2010
Entry Accepted: 11/02/2010
Entry Last Modified: 09/14/2011

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