TY - JOUR A1 - Adam, Lukas A1 - Branda, Martin A1 - Heitsch, Holger A1 - Henrion, René T1 - Solving joint chance constrained problems using regularization and Benders' decomposition JF - Annals of Operations Research N2 - In this paper we investigate stochastic programs with joint chance constraints. We consider discrete scenario set and reformulate the problem by adding auxiliary variables. Since the resulting problem has a difficult feasible set, we regularize it. To decrease the dependence on the scenario number, we propose a numerical method by iteratively solving a master problem while adding Benders cuts. We find the solution of the slave problem (generating the Benders cuts) in a closed form and propose a heuristic method to decrease the number of cuts. We perform a numerical study by increasing the number of scenarios and compare our solution with a solution obtained by solving the same problem with continuous distribution. KW - chance constrained programming KW - optimality conditions KW - regularization KW - Benders cuts KW - gas networks Y1 - U6 - https://doi.org/10.1007/s10479-018-3091-9 VL - 292 SP - 683 EP - 709 ER - TY - JOUR A1 - Adam, Lukas A1 - Henrion, Rene A1 - Outrata, Jiri T1 - On M-stationarity conditions in MPECs and the associated qualification conditions JF - Mathematical Programming N2 - Depending on whether a mathematical program with equilibrium constraints (MPEC) is considered in its original or its enhanced (via KKT conditions) form, the assumed qualification conditions as well as the derived necessary optimality conditions may differ significantly. In this paper, we study this issue when imposing one of the weakest possible qualification conditions, namely the calmness of the perturbation mapping associated with the respective generalized equations in both forms of the MPEC. It is well known that the calmness property allows one to derive the so-called M-stationarity conditions. The restrictiveness of assumptions and the strength of conclusions in the two forms of the MPEC is also strongly related to the qualification conditions on the “lower level”. For instance, even under the Linear Independence Constraint Qualification (LICQ) for a lower level feasible set described by C 1 functions, the calmness properties of the original and the enhanced perturbation mapping are drastically different. When passing to C 1,1 data, this difference still remains true under the weaker Mangasarian-Fromovitz Constraint Qualification, whereas under LICQ both the calmness assumption and the derived optimality conditions are fully equivalent for the original and the enhanced form of the MPEC. After clarifying these relations, we provide a compilation of practically relevant consequences of our analysis in the derivation of necessary optimality conditions. The obtained results are finally applied to MPECs with structured equilibria. KW - equilibrium constraints KW - optimality conditions KW - constraint qualification KW - calmness KW - perturbation mapping Y1 - 2017 ER -