A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming

Author:

Fábián Csaba I.ORCID,Csizmás Edit,Drenyovszki Rajmund,Vajnai Tibor,Kovács Lóránt,Szántai Tamás

Funder

Hungarian Government, co-financed by the European Social Fund

Hungarian Government and co-financed by the European Social Fund

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research,General Decision Sciences

Reference67 articles.

1. Ambartzumian, R., Der Kiureghian, A., Ohanian, V., & Sukiasian, H. (1998). Multinormal probability by sequential conditioned importance sampling: Theory and applications. Probabilistic Engineering Mechanics, 13, 299–308.

2. Arnold, T., Henrion, R., Möller, A., & Vigerske, S. (2014). A mixed-integer stochastic nonlinear optimization problem with joint probabilistic constraints. Pacific Journal of Optimization, 10, 5–20.

3. Benveniste, A., Métivier, M., & Priouret, P. (1993). Adaptive algorithms and stochastic approximations. New York: Springer.

4. Birge, J., & Louveaux, F. (1997). Introduction to stochastic programming. New York: Springer.

5. Boros, E., & Veneziani, P. (2002). Bounds of degree 3 for the probability of the union of events. Technical report, Rutgers Center for Operations Research, RUTCOR Research Report 3-2002.

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