Barycentric Bounds in Stochastic Programming: Theory and Application

  • Karl FrauendorferEmail author
  • Daniel Kuhn
  • Michael Schürle
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 150)


The design and analysis of efficient approximation schemes are of fundamental importance in stochastic programming research. Bounding approximations are particularly popular for providing strict error bounds that can be made small by using partitioning techniques. In this chapter we develop a powerful bounding method for linear multistage stochastic programs with a generalized nonconvex dependence on the random parameters. Thereby, we establish bounds on the recourse functions as well as compact bounding sets for the optimal decisions. We further demonstrate that our bounding methods facilitate the reliable solution of important real-life decision problems. To this end, we solve a stochastic optimization model for the management of nonmaturing accounts and compare the bounds on maximum profit obtained with different partitioning strategies.


Correction Term Stochastic Program Random Parameter Liquidity Risk Linear Stochastic Program 
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.



Daniel Kuhn thanks the Swiss National Science Foundation for financial support.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Karl Frauendorfer
    • 1
    Email author
  • Daniel Kuhn
    • 2
  • Michael Schürle
    • 1
  1. 1.University of St. GallenSt. GallenSwitzerland
  2. 2.Imperial College of Science, Technology and MedicineLondonUK

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