Semiconvex regression for metamodeling-based optimization

Lauren A. Hannah, Warren Buckler Powell, David B. Dunson

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)573-597
Number of pages25
JournalSIAM Journal on Optimization
Issue number2
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Applied Mathematics


  • Asymptotic properties
  • Machine learning
  • Metamodeling
  • Multivariate convex functions
  • Nonparametric regression
  • Simulation optimization


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