@article{MTMT:3315123,
	title = {Emlékezés professzor Prékopa András akadémikusra, halálának első évfordulóján},
	url = {https://m2.mtmt.hu/api/publication/3315123},
	author = {Deák, István and Szántai, Tamás},
	doi = {10.1556/2065.178.2017.12.11},
	journal-iso = {MAGYAR TUDOMÁNY},
	journal = {MAGYAR TUDOMÁNY},
	volume = {178},
	unique-id = {3315123},
	issn = {0025-0325},
	year = {2017},
	eissn = {1588-1245},
	pages = {1599-1605}
}

@article{MTMT:2734907,
	title = {A parallel implementation of an O∗(n4) volume algorithm},
	url = {https://m2.mtmt.hu/api/publication/2734907},
	author = {Mohácsi, László and Deák, István},
	doi = {10.1007/s10100-014-0354-7},
	journal-iso = {CEJOR},
	journal = {CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH},
	volume = {23},
	unique-id = {2734907},
	issn = {1435-246X},
	year = {2015},
	eissn = {1613-9178},
	pages = {925-952}
}

@book{MTMT:2741337,
	title = {Scheduling of Power Generation. A Large-Scale Mixed-Variable Model},
	url = {https://m2.mtmt.hu/api/publication/2741337},
	isbn = {9783319078144},
	author = {Prékopa, András and János, Mayer and Wagnerné Strazicky, Beáta and Deák, István and János, Hoffer and Németh, Ágoston and Béla, Potecz},
	doi = {10.1007/978-3-319-07815-1},
	publisher = {Springer London, Ltd},
	unique-id = {2741337},
	year = {2014}
}

@inproceedings{MTMT:2886297,
	title = {The Effect of the Environmental Legislation on the Transmission System Optimization and Reliability in Hungary},
	url = {https://m2.mtmt.hu/api/publication/2886297},
	author = {Herczeg, András and Deák, István},
	booktitle = {XI. Természet-, Műszaki és Gazdaságtudományok Alkalmazása Nemzetközi Konferencia. Előadások = 11th International Conference on Applications of Natural, Technological and Economic Sciences: Presentations},
	unique-id = {2886297},
	year = {2012},
	pages = {135-142}
}

@article{MTMT:2225345,
	title = {Convex approximations in stochastic programming by semidefinite programming},
	url = {https://m2.mtmt.hu/api/publication/2225345},
	author = {Deák, István and Imre, Pólik and Prékopa, András and Tamás, Terlaky},
	doi = {10.1007/s10479-011-0986-0},
	journal-iso = {ANN OPER RES},
	journal = {ANNALS OF OPERATIONS RESEARCH},
	volume = {200},
	unique-id = {2225345},
	issn = {0254-5330},
	year = {2012},
	eissn = {1572-9338},
	pages = {171-182}
}

@article{MTMT:1998062,
	title = {Computational results of an O(n 4) volume algorithm},
	url = {https://m2.mtmt.hu/api/publication/1998062},
	author = {Lovász, László and Deák, István},
	doi = {10.1016/j.ejor.2011.06.024},
	journal-iso = {EJOR},
	journal = {EUROPEAN JOURNAL OF OPERATIONAL RESEARCH},
	volume = {216},
	unique-id = {1998062},
	issn = {0377-2217},
	abstract = {Recently an O(n 4) volume algorithm has been presented for convex bodies by Lovász and Vempala, where n is the number of dimensions of the convex body. Essentially the algorithm is a series of Monte Carlo integrations. In this paper we describe a computer implementation of the volume algorithm, where we improved the computational aspects of the original algorithm by adding variance decreasing modifications: a stratified sampling strategy, double point integration and orthonormalised estimators. Formulas and methodology were developed so that the errors in each phase of the algorithm can be controlled. Some computational results for convex bodies in dimensions ranging from 2 to 10 are presented as well. © 2011 Elsevier B.V. All rights reserved.},
	keywords = {Algorithms; Computational results; simulation; Monte Carlo; Monte Carlo methods; Markov processes; Volume algorithms; Volume algorithm; Monte Carlo computation; Applied probability},
	year = {2012},
	eissn = {1872-6860},
	pages = {152-161},
	orcid-numbers = {Lovász, László/0000-0001-6596-0465}
}

@inproceedings{MTMT:2582353,
	title = {Solving random linear problems: Expected value linear programming},
	url = {https://m2.mtmt.hu/api/publication/2582353},
	author = {Deák, István},
	booktitle = {Proceedings of the thirteenth International Conference on Civil, Structural and Environmental Engineering Computing},
	doi = {10.4203/ccp.96.93},
	unique-id = {2582353},
	abstract = {A basic procedure in optimization techniques is optimizing a linear function under linear constraints, that is the problem of linear programming. When a linear programming problem has random variables instead of its constant parameters, than we call it a random linear programming problem and reformulate it to obtain a mathematically tractable form. The problem called expected value linear programming is presented here. A method called Successive Regression Approximations, has recently been developed to solve stochastic optimization problems, where some of the parameters are random. Here it is applied to solve random linear problems; we discuss the interpretation of a solution and present the detailed theoretical background for solving expected value linear problems by Successive Regression Approximations. © Civil-Comp Press, 2011.},
	keywords = {Optimization; Problem solving; Regression Analysis; Optimization techniques; Linear programming; Computer aided engineering; Environmental engineering; stochastic systems; Linear functions; Expected values; Stochastic optimizations; Stochastic optimization problems; Regression approximation; Linear programming problem; Linear problems; Linear constraints; Constant parameters; Basic procedure; Successive regression approximations; Stochastic optimization; Random linear problems; Expected value optimization},
	year = {2011}
}

@article{MTMT:1998228,
	title = {Efficiency of Monte Carlo computations in very high dimensional spaces},
	url = {https://m2.mtmt.hu/api/publication/1998228},
	author = {Deák, István},
	doi = {10.1007/s10100-010-0166-3},
	journal-iso = {CEJOR},
	journal = {CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH},
	volume = {19},
	unique-id = {1998228},
	issn = {1435-246X},
	abstract = {A standard measure for comparing different Monte Carlo estimators is the efficiency, which generally thought to be declining with increasing the number of dimensions. Here we give some numerical examples, ranging from one-hundred to one-thousand dimensional integration problems, that contradict this belief. Monte Carlo integrations carried out in one-thousand dimensional spaces is the other nontrivial result reported here. The examples concern the computation of the probabilities of convex sets (polyhedra and hyperellipsoids) in case of multidimensional normal probabilities. © 2010 Springer-Verlag.},
	keywords = {Monte Carlo methods; Multidimensional normal distribution; Probabilities of convex sets; Efficiency of estimators; Comparison of performances},
	year = {2011},
	eissn = {1613-9178},
	pages = {177-189}
}

@article{MTMT:1996996,
	title = {Testing successive regression approximations by large-scale two-stage problems},
	url = {https://m2.mtmt.hu/api/publication/1996996},
	author = {Deák, István},
	doi = {10.1007/s10479-009-0602-8},
	journal-iso = {ANN OPER RES},
	journal = {ANNALS OF OPERATIONS RESEARCH},
	volume = {186},
	unique-id = {1996996},
	issn = {0254-5330},
	abstract = {A heuristic procedure, called successive regression approximations (SRA) has been developed for solving stochastic programming problems. They range from equation solving to probabilistic constrained and two-stage models through a combined model of Prékopa. We show here, that due to enhancements in the computer program, SRA can be used to solve large-scale two-stage problems with 100 first stage decision variables and a 120 dimensional normally distributed random right hand side vector in the second stage problem. A FORTRAN source program and computational results for 124 problems are presented at www.uni-corvinus. hu/~ideak1. © 2009 Springer Science+Business Media, LLC.},
	year = {2011},
	eissn = {1572-9338},
	pages = {83-99}
}

@inproceedings{MTMT:1998277,
	title = {Convergence of Successive Regression Approximations for Solving Noisy Equations},
	url = {https://m2.mtmt.hu/api/publication/1998277},
	author = {Deák, István},
	booktitle = {Proceedings of the Tenth International Conference on Computational Structures Technology},
	doi = {10.4203/ccp.93.209},
	unique-id = {1998277},
	year = {2010}
}