Optimization Online


Obtaining Quadratic Models of Noisy Functions

Aswin Kannan(akannan***at***mcs.anl.gov>)
Stefan Wild(wild***at***mcs.anl.gov)

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.

Download: [PDF]

Entry Submitted: 10/24/2012
Entry Accepted: 10/24/2012
Entry Last Modified: 10/24/2012

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society