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Distributionally Robust Discrete Optimization with Entropic Value-at-Risk

Long Daniel Zhuoyu(zylong***at***se.cuhk.edu.hk)
QI Jin(qijin***at***nus.edu.sg)

Abstract: We study the discrete optimization problem under the distributionally robust framework. We optimize the Entropic Value-at-Risk, which is a coherent risk measure and is also known as Bernstein approximation for the chance constraint. We propose an efficient approximation algorithm to resolve the problem via solving a sequence of nominal problems. The computational results show that the number of nominal problems required to be solved is small under various distributional information sets.

Keywords: robust optimization; discrete optimization; coherent risk measure

Category 1: Robust Optimization

Category 2: Integer Programming (0-1 Programming )

Citation:

Download: [PDF]

Entry Submitted: 05/06/2014
Entry Accepted: 05/06/2014
Entry Last Modified: 05/06/2014

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