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Risk-averse two-stage stochastic linear programming: modeling and decomposition. (English) Zbl 1218.90145
Summary: We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as a composition of conditional risk measures. We analyze properties of the problem and derive necessary and sufficient optimality conditions. Next, we construct a new decomposition method for solving the problem that exploits the composite structure of the objective function. We illustrate its performance on a portfolio optimization problem.

MSC:
90C15 Stochastic programming
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