Semiconvex regression for metamodeling-based optimization
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.
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Related Subject Headings
- Operations Research
- 4901 Applied mathematics
- 0103 Numerical and Computational Mathematics
- 0102 Applied Mathematics
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Operations Research
- 4901 Applied mathematics
- 0103 Numerical and Computational Mathematics
- 0102 Applied Mathematics