Semiconvex regression for metamodeling-based optimization

Published

Journal Article

Stochastic search involves finding a set of controllable parameters that minimizes an unknown objective function using a set of noisy observations. We consider the case when the unknown function is convex and a metamodel is used as a surrogate objective function. Often he data are non-i.i.d. and include an observable state variable, such as applicant information in a loan rate decision problem. State information is difficult to incorporate into convex models. We propose a new semiconvex regression method that is used to produce a convex metamodel in the presence of a state variable. We show consistency for this method. We demonstrate its effectiveness for metamodeling on a set of synthetic inventory management problems and a large real-life auto loan dataset. © 2014 Society for Industrial and Applied Mathematics.

Full Text

Duke Authors

Cited Authors

  • Hannah, LA; Powell, WB; Dunson, DB

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 24 / 2

Start / End Page

  • 573 - 597

International Standard Serial Number (ISSN)

  • 1052-6234

Digital Object Identifier (DOI)

  • 10.1137/130907070

Citation Source

  • Scopus