Obtaining Quadratic Models of Noisy Functions
Abstract: When derivatives of a nonlinear objective function are unavailable, many derivative- free optimization algorithms rely on interpolation-based models of the function. But what if the function values are contaminated by noise, as in most of the simulation- based problems typically encountered in this area? We propose to obtain linear and quadratic models by using knowledge of the level of noise in a function. We develop an efficient algorithm for obtaining the model coefficients, and we analyze the properties of the corresponding quadratic program.
Category 1: Nonlinear Optimization (Other )
Category 2: Nonlinear Optimization (Quadratic Programming )
Citation: Argonne National Laboratory Preprint ANL/MCS-P1975-1111, September 2011.
Entry Submitted: 10/24/2012
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