@article{MTMT:30415406, title = {A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming}, url = {https://m2.mtmt.hu/api/publication/30415406}, author = {Fábián, Csaba and Gurka Dezsőné Csizmás, Edit Margit and Drenyovszki, Rajmund and Vajnai, Tibor and Kovács, Lóránt and Szántai, Tamás}, doi = {10.1007/s10479-019-03143-z}, journal-iso = {ANN OPER RES}, journal = {ANNALS OF OPERATIONS RESEARCH}, volume = {Online first}, unique-id = {30415406}, issn = {0254-5330}, year = {2019}, eissn = {1572-9338}, pages = {1-32}, orcid-numbers = {Fábián, Csaba/0000-0002-9446-1566; Gurka Dezsőné Csizmás, Edit Margit/0000-0003-4397-1758; Drenyovszki, Rajmund/0000-0002-9462-2729} } @article{MTMT:2584126, title = {The integer programming background of a stochastic integer programming algorithm of Dentcheva-Prekopa-Ruszczynski}, url = {https://m2.mtmt.hu/api/publication/2584126}, author = {Vizvári, Béla}, doi = {10.1080/1055678021000034017}, journal-iso = {OPTIM METHOD SOFTW}, journal = {OPTIMIZATION METHODS & SOFTWARE}, volume = {17}, unique-id = {2584126}, issn = {1055-6788}, abstract = {This paper analyzes the algorithmic tools presented in [D. Dentcheva, A. Prekopa and A. Ruszczynski. Bounds for Probabilistic Integer Programming Problems, RUTCOR, Rutgers University, NJ, RRR 31-99; D. Dentcheva, A. Prekopa and A. Ruszczynski (2000). Concavity and efficient points of discrete distributions in probabilistic programming. Mathematical Programming, Ser. A , 89 , 55-77]. The emphasis is on the choice of Lagrange multipliers and the solution for the special case of independent random variables.}, year = {2002}, eissn = {1029-4937}, pages = {543-559}, orcid-numbers = {Vizvári, Béla/0000-0002-1349-1035} } @article{MTMT:3230053, title = {Concavity and efficient points of discrete distributions in probabilistic programming}, url = {https://m2.mtmt.hu/api/publication/3230053}, author = {Dentcheva, D and Prékopa, András and Ruszczyński, A}, doi = {10.1007/s101070000178}, journal-iso = {MATH PROGRAM}, journal = {MATHEMATICAL PROGRAMMING}, volume = {89}, unique-id = {3230053}, issn = {0025-5610}, abstract = {We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal value of probabilistically constrained stochastic programming problems with discrete random variables. The results are illustrated with numerical examples. © Springer-Verlag 2000.}, keywords = {Column generation; DISCRETE DISTRIBUTIONS; Probabilistic programming; Generalized concavity}, year = {2000}, eissn = {1436-4646}, pages = {55-77} } @CONFERENCE{MTMT:2617153, title = {Probabilistic constrained programming and distributions with given marginals.}, url = {https://m2.mtmt.hu/api/publication/2617153}, author = {Szántai, Tamás}, booktitle = {Distributions with given marginals and moment problems, Proceedings of the 3rd Conference on ''Distributions with Given Marginals and Moment Problems'', held at Czech Agricultural University, Prague, Czech Republic, September 2-6, 1996, eds}, unique-id = {2617153}, year = {1997}, pages = {205-210} } @CONFERENCE{MTMT:2694566, title = {A computer code for solution of probabilistic constrained stochastic programming problems}, url = {https://m2.mtmt.hu/api/publication/2694566}, author = {Szántai, Tamás}, booktitle = {Numerical Techniques for Stochastic Programming Problems, Springer Series in Computational Mathematics}, unique-id = {2694566}, year = {1988}, pages = {229-235} } @article{MTMT:2617018, title = {EVALUATION OF A SPECIAL MULTIVARIATE GAMMA-DISTRIBUTION FUNCTION. Stochastic Programming 84 Part I.}, url = {https://m2.mtmt.hu/api/publication/2617018}, author = {Szántai, Tamás}, journal-iso = {MATH PROGRAM STUD}, journal = {MATHEMATICAL PROGRAMMING STUDY}, volume = {27}, unique-id = {2617018}, issn = {0303-3929}, abstract = {In this paper we describe two different methods for the calculation of the bivariate gamma probability distribution function. One of them is based on a direct numerical integration and the other on a series expansion in terms of Laguerre polynomials. In the multivariate case we propose a Monte Carlo method. Our method can be used for other types of multivariate probability distributions too. In the special case of the multivariate normal distribution the computer experiments show that our method has the same efficiency as other known methods. We briefly describe the possible applications of the proposed algorithms in stochastic programming.}, year = {1986}, pages = {1-16} } @mastersthesis{MTMT:2617134, title = {Többdimenziós valószínűség-eloszlásokkal kapcsolatos valószínűségek numerikus meghatározásáról}, url = {https://m2.mtmt.hu/api/publication/2617134}, author = {Szántai, Tamás}, unique-id = {2617134}, year = {1985} } @{MTMT:30449452, title = {The stabil stochastic programming model and its experimental application to the electrical energy sector of the Hungarian economy}, url = {https://m2.mtmt.hu/api/publication/30449452}, author = {Prékopa, András and Ganczer, Sándor and Deák, István and Patyi, Károly}, booktitle = {Stochastic programming}, unique-id = {30449452}, year = {1980}, pages = {369-385} } @article{MTMT:2617016, title = {An optimal regulation of a storage level with application to the water level regulation of a lake}, url = {https://m2.mtmt.hu/api/publication/2617016}, author = {Prékopa, András and Szántai, Tamás}, doi = {10.1016/0377-2217(79)90137-1}, journal-iso = {EJOR}, journal = {EUROPEAN JOURNAL OF OPERATIONAL RESEARCH}, volume = {3}, unique-id = {2617016}, issn = {0377-2217}, abstract = {In [10] a system of stochastic programming models was introduced for the optimal control of a storage level. Each model in this system serves to determine the optimal policy for only one period ahead though the time horizon consists of many future periods. The optimal control thus obtained can be considered an open loop control methodology. The main purpose of this paper is to present an application by giving an optimal control method for the regulation of the water level of Lake Balaton in Hungary. By solving almost 600 stochastic programming problems we analyze what would have happened if we had controlled the water level using our method between 1922 and 1970, where one decision period is one month. The numerical results show that the proposed control methodology works quite well in this case. © 1979.}, year = {1979}, eissn = {1872-6860}, pages = {175-189} } @article{MTMT:2617014, title = {Flood control reservoir system design using stochastic programming}, url = {https://m2.mtmt.hu/api/publication/2617014}, author = {Prékopa, András and Szántai, Tamás}, journal-iso = {MATH PROGRAM STUD}, journal = {MATHEMATICAL PROGRAMMING STUDY}, volume = {9}, unique-id = {2617014}, issn = {0303-3929}, year = {1978}, pages = {138-151} } @article{MTMT:2617015, title = {NEW MULTIVARIATE GAMMA DISTRIBUTION AND ITS FITTING TO EMPIRICAL STREAMFLOW DATA}, url = {https://m2.mtmt.hu/api/publication/2617015}, author = {Prékopa, András and Szántai, Tamás}, doi = {10.1029/WR014i001p00019}, journal-iso = {WATER RESOUR RES}, journal = {WATER RESOURCES RESEARCH}, volume = {14}, unique-id = {2617015}, issn = {0043-1397}, abstract = {A new multivariate gamma distribution is presented which can successfully be fitted to empirical data where the one‐dimensional marginal distributions are gamma distributions with prescribed parameters and the correlations are nonnegative. It is not intended to give explicit formulae either for the joint density or for the joint characteristic function of the random variables. Our representation of the individual gamma‐distributed random variables will be used for simulation, with the aid of which we approximate probabilities of sets in higher‐dimensional spaces. Since streamflow and other hydrological data frequently follow gamma distribution and also they are frequently stochastically dependent, our multivariate distribution and fitting technique seems to be of particular interest from the hydrological point of view. Copyright 1978 by the American Geophysical Union.}, year = {1978}, eissn = {1944-7973}, pages = {19-24} }