0460909 20240103212422.520160721235959.9 84966573884 000380275600004 10.1007/s10957-016-0943-9 18 s. P cav_un_epca*02570610022-32391702 (2016)419-436Springer Chance constrained programming Optimality conditions Regularization Algorithms Free MATLAB codes cav_un_auth*0309054 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory Adam Lukáš UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0280972 Ekonometrie Department of Econometrics E E Department of Decision Making Theory Branda Martin UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/MTR/adam-0460909.pdf cav_un_auth*0321507 GA15-00735S GA ČR We deal with chance constrained problems with differentiable nonlinear random functions and discrete distribution. We allow nonconvex functions both in the constraints and in the objective. We reformulate the problem as a mixed-integer nonlinear program and relax the integer variables into continuous ones. We approach the relaxed problem as a mathematical problem with complementarity constraints and regularize it by enlarging the set of feasible solutions. For all considered problems, we derive necessary optimality conditions based on Fréchet objects corresponding to strong stationarity. We discuss relations between stationary points and minima. We propose two iterative algorithms for finding a stationary point of the original problem. The first is based on the relaxed reformulation, while the second one employs its regularized version. BB 2017 2 RVO:67985556 http://hdl.handle.net/11104/0261533 S 1* Article Operations Research Management Science|Mathematics Applied MATHEMATICS.APPLIED|OPERATIONSRESEARCH&MANAGEMENTSCIENCE 1.579 0.272 999.9 0.01183 0.837 5771 1.092 Q1 1.121 224 Q2 68.758 58.6 Q2 Q2 71.961 2016 84966573884 SCOPUS 000380275600004 WOS cav_un_epca*0257061 Journal of Optimization Theory and Applications 0022-3239 1573-2878 Roč. 170 č. 2 2016 419 436 Springer