000196183 001__ 196183
000196183 005__ 20190316235832.0
000196183 0247_ $$2doi$$a10.1007/s10957-012-0264-6
000196183 022__ $$a1573-2878
000196183 037__ $$aARTICLE
000196183 245__ $$aA Polynomial-Time Solution Scheme for Quadratic Stochastic Programs
000196183 260__ $$c2013
000196183 269__ $$a2013
000196183 336__ $$aJournal Articles
000196183 520__ $$aWe consider quadratic stochastic programs with random recourse—a class of problems which is perceived to be computationally demanding. Instead of using mainstream scenario tree-based techniques, we reduce computational complexity by restricting the space of recourse decisions to those linear and quadratic in the observations, thereby obtaining an upper bound on the original problem. To estimate the loss of accuracy of this approach, we further derive a lower bound by dualizing the original problem and solving it in linear and quadratic recourse decisions. By employing robust optimization techniques, we show that both bounding problems may be approximated by tractable conic programs.
000196183 6531_ $$aDecision rule approximation
000196183 6531_ $$aRobust optimization
000196183 6531_ $$aQuadratic stochastic programming
000196183 6531_ $$aConic programming
000196183 700__ $$aRocha, Paula
000196183 700__ $$0247589$$g239987$$aKuhn, Daniel
000196183 773__ $$j158$$tJournal of Optimization Theory and Applications$$k2$$q576-589
000196183 8564_ $$uhttp://link.springer.com/article/10.1007/s10957-012-0264-6$$zURL
000196183 909C0 $$xU12788$$0252496$$pRAO
000196183 909CO $$qGLOBAL_SET$$pCDM$$ooai:infoscience.tind.io:196183$$particle
000196183 917Z8 $$x112541
000196183 937__ $$aEPFL-ARTICLE-196183
000196183 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000196183 980__ $$aARTICLE