0376766 20240903170624.520120511235959.9 000301269800006 18 s. cav_un_epca*02971630023-5954481 (2012)105-122Ústav teorie informace a automatizace AV ČR, v. v. i. chance constrained problems penalty functions asymptotic equivalence sample approximation technique investment problem cav_un_auth*0280972 Branda Martin Ekonometrie Department of Econometrics E E UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/E/branda-chance constrained problems penalty reformulation and performance of sample approximation technique.pdf GBP402/12/G097 GA ČR CZ cav_un_auth*0281000 CEZ:AV0Z10750506 We explore reformulation of nonlinear stochastic programs with several joint chance constraints by stochastic programs with suitably chosen penalty-type objectives. We show that the two problems are asymptotically equivalent. Simpler cases with one chance constraint and particular penalty functions were studied in [6,11]. The obtained problems with penalties and with a fixed set of feasible solutions are simpler to solve and analyze then the chance constrained programs. We discuss solving both problems using Monte-Carlo simulation techniques for the cases when the set of feasible solution is finite or infinite bounded. The approach is applied to a financial optimization problem with Value at Risk constraint, transaction costs and integer allocations. We compare the ability to generate a feasible solution of the original chance constrained problem using the sample approximations of the chance constraints directly or via sample approximation of the penalty function objective. 2013 BB 1 4 O 4o 20231122135039.7 http://hdl.handle.net/11104/0209085 COMPUTERSCIENCECYBERNETICS 0.548 0.054 9.6 0.00164 0.284 536 74 21 0.410 20.28 0.78 Q2 30.653 16.667 Q3 Q3 2012 Soubory v repozitáři: 0376766.pdf 000301269800006 WOS cav_un_epca*0297163 Kybernetika 0023-5954 Roč. 48 č. 1 2012 105 122 Ústav teorie informace a automatizace AV ČR, v. v. i.