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.

## Citation

Technical Report, Department of Mathematics and Statistics, Curtin University, Bentley, Australia, Feb. 2016

## Article

View Quadratic Two-Stage Stochastic Optimization with Coherent Measures of Risk