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A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem

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Abstract

This paper deals with the problem of scenario tree reduction for stochastic programming problems. In particular, a reduction method based on cluster analysis is proposed and tested on a portfolio optimization problem. Extensive computational experiments were carried out to evaluate the performance of the proposed approach, both in terms of computational efficiency and efficacy. The analysis of the results shows that the clustering approach exhibits good performance also when compared with other reduction approaches.

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Authors and Affiliations

  1. Department of Mechanical, Energy and Management Engineering, University of Calabria, Via P. Bucci Cubo 41/C, 87036, Rende, CS, Italy

    Patrizia Beraldi & Maria Elena Bruni

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  1. Patrizia Beraldi
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  2. Maria Elena Bruni
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Correspondence to Patrizia Beraldi.

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Beraldi, P., Bruni, M.E. A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem. TOP 22, 934–949 (2014). https://doi.org/10.1007/s11750-013-0305-9

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  • DOI: https://doi.org/10.1007/s11750-013-0305-9

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