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Annals of Operations Research

, Volume 45, Issue 1, pp 59–76 | Cite as

Multi-stage stochastic linear programs for portfolio optimization

  • George B. Dantzig
  • Gerd Infanger
Article

Abstract

The paper demonstrates how multi-period portfolio optimization problems can be efficiently solved as multi-stage stochastic linear programs. A scheme based on a blending of classical Benders decomposition techniques and a special technique, called importance sampling, is used to solve this general class of multi-stochastic linear programs. We discuss the case where stochastic parameters are dependent within a period as well as between periods. Initial computational results are presented.

Keywords

Computational Result General Class Special Technique Portfolio Optimization Importance Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© J.C. Baltzer AG, Science Publishers 1993

Authors and Affiliations

  • George B. Dantzig
    • 1
  • Gerd Infanger
    • 1
  1. 1.Department of Operations ResearchStanford UniversityStanfordUSA

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