- Convex approximations in stochastic programming by semidefinite programming István Deák(istvan.deakuni-corvinus.hu) Imre Pólik(imrepolik.net) András Prékopa(andras.prekopagmail.com) Tamás Terlaky(terlakylehigh.edu) Abstract: The following question arises in stochastic programming: how can one approximate a noisy convex function with a convex quadratic function that is optimal in some sense. Using several approaches for constructing convex approximations we present some optimization models yielding convex quadratic regressions that are optimal approximations in $L_1$, $L_\infty$ and $L_2$ norm. Extensive numerical experiments to investigate the behaviour of the proposed methods are also performed. Keywords: convex approximation, stochastic optimization, successive regression approximations, semidefinite optimization Category 1: Stochastic Programming Category 2: Linear, Cone and Semidefinite Programming (Semi-definite Programming ) Citation: Download: [PDF]Entry Submitted: 04/20/2010Entry Accepted: 04/20/2010Entry Last Modified: 04/20/2010Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society.