%0 Journal Article
%D 2011
%T Obtaining Quadratic Models of Noisy Functions
%A A. Kannan
%A S. M. Wild
%X 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.
%8 11/2011
%G eng
%1 http://www.mcs.anl.gov/papers/P1975-1111.pdf