@inbook{4150db51749244c18a1142959f4d03a8,
title = "Convergence Issues in Stochastic Optimization",
abstract = "In this chapter, we prove a convergence theorem for appropriate notions of noise and information convergence in closed-loop stochastic optimization problems. The main Theorem is based on epi-convergence results, which take into account constraints defined by characteristic functions of sets.",
keywords = "Convergence Notions, Measurement Constraints, Mosco Convergence, Normal Integrand, Random Variable Approximations",
author = "Pierre Carpentier and Chancelier, \{Jean Philippe\} and Guy Cohen and \{De Lara\}, Michel",
note = "Publisher Copyright: {\textcopyright} 2015, Springer International Publishing Switzerland.",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-18138-7\_8",
language = "English",
series = "Probability Theory and Stochastic Modelling",
publisher = "Springer Nature",
pages = "211--252",
booktitle = "Probability Theory and Stochastic Modelling",
}