Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2018
Abstract
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.
Keywords
Conic duality, Quadratic programs, Risk measures, Stochastic optimization
Discipline
Operations and Supply Chain Management | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Operations Management
Publication
Mathematical Programming
Volume
168
Issue
1-2
First Page
599
Last Page
613
ISSN
0025-5610
Identifier
10.1007/s10107-017-1131-x
Publisher
Springer Verlag (Germany)
Citation
SUN, Jie; LIAO, Li-Zhi; and RODRIGUES, Brian.
Quadratic two-stage stochastic optimization with coherent measures of risk. (2018). Mathematical Programming. 168, (1-2), 599-613.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/5155
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10107-017-1131-x
Included in
Operations and Supply Chain Management Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
