A new scheme to cope with two-stage stochastic optimization problems uses a risk measure as the objective function of the recourse action, where the risk measure is defined as the worst-case expected values over a set of constrained distributions. This paper develops an approach to deal with the case where both the first and second stage objective functions are convex linear-quadratic. It is shown that under a standard set of regularity assumptions, this two-stage quadratic stochastic optimization problem with measures of risk is equivalent to a conic optimization problem that can be solved in polynomial time.
Conic duality, Quadratic programs, Risk measures, Stochastic optimization
Operations and Supply Chain Management | Operations Research, Systems Engineering and Industrial Engineering
Springer Verlag (Germany)
SUN, Jie; LIAO, Li-Zhi; and RODRIGUES, Brian Charles.
Quadratic two-stage stochastic optimization with coherent measures of risk. (2017). Mathematical Programming. 1-15. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/5155
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.