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Sample average approximation (SAA) is a tractable approach to deal with thechance constrained programming, a challenging issue in stochastic programming.The constraint is usually characterized by the 0/1 loss function which resultsin enormous difficulties in designing numerical algorithms. Most existingmethods have been created based on the SAA reformulation, such as binaryinteger programming or the relaxation, and no viable algorithms have beendeveloped to tackle SAA directly, not to mention theoretical guarantees. Inthis paper, we investigate a general 0/1 constrained optimization that includesSAA as a special case and thus provide a new way to address SAA. We first showthat the new model has some advantageous statistic properties. Then by derivingthe Bouligand tangent and Frechet normal cones of the 0/1 constraint, weestablish several optimality conditions including the one that can beequivalently expressed by a system of equations, thereby allowing us to designa smoothing Newton type method. We show that the proposed algorithm has alocally quadratic convergence rate and nice numerical performance in comparisonwith two select algorithms and GUROBI.

0/1 Constrained Optimization Solving Sample Average Approximation for Chance Constrained Programming