Chance constrained programs with Gaussian mixture models

Zhaolin Hu, Wenjie Sun and Shushang Zhu

IISE Transactions, 2022, vol. 54, issue 12, 1117-1130

Abstract: In this article, we discuss input modeling and solution techniques for several classes of Chance constrained programs (CCPs). We propose to use a Gaussian Mixture Model (GMM) to fit the data available and to model the randomness. We demonstrate the merits of using a GMM. We consider several scenarios that arise from practical applications and analyze how the problem structures could embrace alternative optimization techniques. More specifically, for several scenarios, we study how to assess the gradient of the chance constraint and incorporate the results into gradient-based nonlinear optimization algorithms, and for a class of CCPs, we propose a spatial branch-and-bound procedure and solve the problems to global optimality. We also conduct numerical experiments to test the efficiency of our approach and propose an example of hedge fund portfolio to illustrate the practical application of the method.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2021.2001608 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:54:y:2022:i:12:p:1117-1130

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2021.2001608

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().