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Solving multistage asset investment problems by the sample average approximation method
Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Optimization .
School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205, United States.
2006 (English)In: Mathematical programming, ISSN 0025-5610, Vol. 108, no 2-3, 571-595 p.Article in journal (Refereed) Published
Abstract [en]

The vast size of real world stochastic programming instances requires sampling to make them practically solvable. In this paper we extend the understanding of how sampling affects the solution quality of multistage stochastic programming problems. We present a new heuristic for determining good feasible solutions for a multistage decision problem. For power and log-utility functions we address the question of how tree structures, number of stages, number of outcomes and number of assets affect the solution quality. We also present a new method for evaluating the quality of first stage decisions.

Place, publisher, year, edition, pages
2006. Vol. 108, no 2-3, 571-595 p.
Keyword [en]
Asset allocation, Monte Carlo sampling, SAA method, Statistical bounds, Stochastic programming
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-50188DOI: 10.1007/s10107-006-0723-7OAI: diva2:271084
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2011-01-11

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Blomvall, Jörgen
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