TY - EJOUR
DO - 10.1007/s10957-012-0264-6
AB - We consider quadratic stochastic programs with random recourseâ€”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.
T1 - A Polynomial-Time Solution Scheme for Quadratic Stochastic Programs
IS - 2
DA - 2013
AU - Rocha, Paula
AU - Kuhn, Daniel
JF - Journal of Optimization Theory and Applications
SP - 576-589
VL - 158
EP - 576-589
ID - 196183
KW - Decision rule approximation
KW - Robust optimization
KW - Quadratic stochastic programming
KW - Conic programming
SN - 1573-2878
UR - http://link.springer.com/article/10.1007/s10957-012-0264-6
ER -