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Stochastic Decomposition and Extensions

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Stochastic Programming

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 150))

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Abstract

This chapter presents some extensions associated with stochastic decomposition (SD). Specifically, we study two issues: (a) are there conditions under which the regularized version of SD generates a unique solution? and (b) in cases where a user is willing to sacrifice optimality, is there a way to modify the SD algorithm so that a user can tradeoff solution times with solution quality? This chapter presents our preliminary approach to address these questions.

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Acknowledgments

We are grateful to the referee for a thorough report. This research was funded, in part, by NSF grant CMMI - 0900070, and AFOSR grant FA9550-08-1-0117.

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Authors and Affiliations

  1. The Data Driven Decisions Lab, Industrial, Welding, and Systems Engineering, The Ohio State University, Columbus, OH, 43210, USA

    Suvrajeet Sen

  2. Department of Systems and Industrial Engineering, The MORE Institute, The University of Arizona, Tucson, AZ, 85721, USA

    Suvrajeet Sen & Zhihong Zhou

  3. Area of Operations Management, DeGroote School of Business McMaster University, Hamilton, Ontario, L8S4M4, Canada

    Kai Huang

Authors
  1. Suvrajeet Sen
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  2. Zhihong Zhou
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  3. Kai Huang
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Corresponding author

Correspondence to Suvrajeet Sen .

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Editors and Affiliations

  1. Dept. Management Science & Engineering, Stanford University, Stanford, 94305, California, USA

    Gerd Infanger

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Sen, S., Zhou, Z., Huang, K. (2010). Stochastic Decomposition and Extensions. In: Infanger, G. (eds) Stochastic Programming. International Series in Operations Research & Management Science, vol 150. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1642-6_4

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