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Discrete‐time approximation for stochastic optimal control problems under the G ‐expectation framework
Author(s) -
Jiang Lianzi
Publication year - 2021
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2814
Subject(s) - piecewise , convergence (economics) , discrete time and continuous time , constant (computer programming) , class (philosophy) , mathematical optimization , stochastic approximation , mathematics , control (management) , optimal control , stochastic control , discrete time stochastic process , computer science , stochastic optimization , continuous time stochastic process , mathematical analysis , economics , statistics , computer security , artificial intelligence , key (lock) , programming language , economic growth
In this article, we propose a class of discrete‐time approximation schemes for stochastic optimal control problems under the G ‐expectation framework. The proposed schemes are constructed recursively based on piecewise constant policy. We prove the convergence of the discrete schemes and determine the convergence rates. Several numerical examples are presented to illustrate the effectiveness of the obtained results.