STOCHASTIC PROGRAMMING WHEN OBJECTIVE FUNCTION AND CONSTRAINTS CONTAIN CONDITIONAL VALUE AT RISK
Journal Title: Jaunųjų mokslininkų darbai - Year 2014, Vol 42, Issue 2
Abstract
The method of sequential quadratic programming is proposed to solve the risk aversion optimization problem by a series of Monte-Carlo estimators. The sequential simulation-based approach for stochastic programming with CVaR has been tested by the Monte-Carlo method by simulating the piecewise-linear test functions. The results of the Monte Carlo simulation illustrate the convergence of this approach and the ability to solve the stochastic programming problems, where CVaR is included into both objective function and constraints, at the proper accuracy treated in a statistical manner. Key words: risk aversion optimization problem, Monte – Carlo method, convergence.
Authors and Affiliations
Valerijonas Dumskis, Leonidas Sakalauskas
Keywords
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