Primal and dual robust counterparts of uncertain linear programs: an application to portfolio selection

Subject Areas : Mathematical Optimization

P Hanafizadeh ¹ , A Seifi ² , K Ponnambalam ³

1 - Assistant Professor, Department of Industrial Management, Allame Tabataba'ee University, Tehran, Iran
2 - Associate Professor, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
3 - Professor, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada

Received: 2015-05-19 Accepted : 2015-05-19 Published : 2006-03-01

Keywords: Portfolio Selection, Linear programming, Robust Optimization, Data uncertainty,

Abstract :

This paper proposes a family of robust counterpart for uncertain linear programs (LP) which is obtained for a general definition of the uncertainty region. The relationship between uncertainty sets using norm bod-ies and their corresponding robust counterparts defined by dual norms is presented. Those properties lead us to characterize primal and dual robust counterparts. The researchers show that when the uncertainty region is small the corresponding robust counterpart is less conservative than the one for a larger region. Therefore, the model can be adjusted by choosing an appropriate norm body and the radius of the uncertainty region. We show how to apply a robust modeling approach to single and multi-period portfolio selection problems and illustrate the model properties with numerical examples.