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This article describes a novel approach to chance-constrained programmingbased on the sample average approximation (SAA) method. Recent work focuses onheuristic approximations to the SAA problem and we introduce a novel approachwhich improves on some existing methods. Our Active Set method allows one tosolve SAAs of chance-constrained programs with very large numbers of scenariosquickly. We demonstrate that increasing the number of scenarios is moreimportant than improving accuracy with small numbers of scenarios.  We use an example of the portfolio selection problem to demonstrate therelative performance of previous and new methods. Extending the Active Setmethod to an integer-programming model further highlights its applicability andfurther improves over previous approaches.

Active Set methods for solving large sample average approximations of chance constrained optimisation problems