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Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition

Naomi Miller(nmiller***at***rutcor.rutgers.edu)
Andrzej Ruszczynski(rusz***at***business.rutgers.edu)

Abstract: 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 two decomposition methods for solving the problem. The first method is based on the generic cutting plane approach, while the second method exploits the composite structure of the objective function. We illustrate their performance on a portfolio optimization problem.

Keywords: stochastic programming, two-stage models, risk measures

Category 1: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 08/29/2009
Entry Accepted: 08/29/2009
Entry Last Modified: 08/29/2009

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