On Optimality Functions in Stochastic Programming and Applications
dc.contributor.author | Royset, J.O. | |
dc.date.accessioned | 2014-05-30T22:12:25Z | |
dc.date.available | 2014-05-30T22:12:25Z | |
dc.date.issued | 2010-10-06 | |
dc.identifier.uri | https://hdl.handle.net/10945/41759 | |
dc.description.abstract | Optimality functions define stationarity in nonlinear programming, semi-infinite optimization, and optimal control in some sense. In this paper, we consider optimality functions for stochastic programs with nonlinear, possibly nonconvex, expected value objective and constraint functions. We show that an optimality function directly relates to the difference in function values at a candidate point and a local minimizer. We construct confidence intervals for the value of the optimality function at a candidate point and, hence, provide a quantitative measure of solution quality. Based on sample average approximations, we develop two algorithms for classes of stochastic programs that include CVaR-problems and utilize optimality functions to select sample sizes as well as “active” sample points in an active-set strategy. Numerical tests illustrate the procedures. | en_US |
dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. | en_US |
dc.title | On Optimality Functions in Stochastic Programming and Applications | en_US |
dc.type | Article | en_US |
dc.contributor.department | Operations Research | |
dc.subject.author | Stochastic programming | en_US |
dc.subject.author | nonlinear programming | en_US |
dc.subject.author | optimality conditions | en_US |
dc.subject.author | validation analysis | en_US |
dc.subject.author | algorithms | en_US |