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Integration by convex optimization

Author(s):

Riccardo Moriconi
Conference/Journal:

Semester Thesis, HS14 (10456)
Abstract:

High dimensional integration is an important computational building block in a myriad of application areas. Consider for instance risk assessment of financial products, signal error rates in communication protocols or classifier correctness in machine learning. Unfortunately, high dimensional integration is computationally demanding in general. Instead of trying to approximate these formidable problems in a heuristic way, as done for instance with Monte Carlo based methods, this project will consider an alternative approach based on convex optimization problems bounding these difficult high dimensional integrals.

Supervisors: Bart Van Parys, Paul Goulart, Manfred Morari

Year:

2014
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:

M. Morari

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2014:IFA_4992
}
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