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Stochastic programming with Quasi-Monte Carlo methods


M. Thély

Master Thesis, FS16

We consider a general stochastic programming problem, where the objective function is an integral that is not analytically available and we therefore resort to an approximation. We study modern integration techniques, among which quasi-Monte Carlo (QMC) methods emerge as the most suitable. We introduce important aspects of QMC theory, such as weighted spaces and randomly shifted lattice rules. We highlight the conditions on the integrand and the difficulties for the application of QMC techniques. The presented approximation schemes are tested and compared to traditional Monte Carlo methods on three maximum entropy estimation problems in multiple dimensions.

Supervisors: Tobias Sutter, Dr. Peyman Mohajerin Esfahani, John Lygeros


Type of Publication:

(12)Diploma/Master Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2017:IFA_5641
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