Semiconvex regression for metamodeling-based optimization
Journal Article (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