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On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs

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  • A. Ruszczynski

Abstract

A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes corresponding to stages. In both cases the method has favorable convergence properties and a structure which makes it convenient for parallel computing environments.

Suggested Citation

  • A. Ruszczynski, 1994. "On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs," Working Papers wp94005, International Institute for Applied Systems Analysis.
    • Handle: RePEc:wop:iasawp:wp94005
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    Cited by:

    1. Jesús Latorre & Santiago Cerisola & Andrés Ramos & Rafael Palacios, 2009. "Analysis of stochastic problem decomposition algorithms in computational grids," Annals of Operations Research, Springer, vol. 166(1), pages 355-373, February.
    2. Diana Barro & Elio Canestrelli, 2005. "Time and nodal decomposition with implicit non-anticipativity constraints in dynamic portfolio optimization," GE, Growth, Math methods 0510011, University Library of Munich, Germany.
    3. Diana Barro & Elio Canestrelli, 2011. "Combining stochastic programming and optimal control to solve multistage stochastic optimization problems," Working Papers 2011_24, Department of Economics, University of Venice "Ca' Foscari", revised 2011.
    4. K. Kiwiel & C.H. Rosa & A. Ruszczynski, 1995. "Decomposition via Alternating Linearization," Working Papers wp95051, International Institute for Applied Systems Analysis.
    5. M. Makowski & L. Somlyody & D. Watkins, 1995. "Multiple Criteria Analysis for Regional Water Quality Management: the Nitra River Case," Working Papers wp95022, International Institute for Applied Systems Analysis.

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