Math @ Duke

Publications [#257838] of David B. Dunson
search arxiv.org.Papers Published
 Hannah, LA; Powell, WB; Dunson, DB, Semiconvex regression for metamodelingbased optimization,
Siam Journal on Optimization, vol. 24 no. 2
(January, 2014),
pp. 573597, Society for Industrial & Applied Mathematics (SIAM), ISSN 10526234 [doi]
(last updated on 2019/06/17)
Abstract: 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 noni.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 reallife auto loan dataset. © 2014 Society for Industrial and Applied Mathematics.


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