Quadratic two-stage stochastic optimization with coherent measures of risk

Jie Sun*, Lizhi Liao, Brian Rodrigues

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

11 Citations (Scopus)
38 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)599-613
Number of pages15
JournalMathematical Programming
Issue number1-2
Early online date4 Mar 2017
Publication statusPublished - Mar 2018

Scopus Subject Areas

  • Software
  • Mathematics(all)

User-Defined Keywords

  • Conic duality
  • Quadratic programs
  • Risk measures
  • Stochastic optimization


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