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Tree Approximations of Dynamic Stochastic Programs. Edition No. 1

  • ID: 1902800
  • September 2008
  • 176 Pages
  • VDM Publishing House
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Dynamic multistage stochastic optimization programs
offer a possibility to include uncertainty into
optimization models, providing a contemporary set of
tools for modern management sciences with wide range
of applications.

In order to solve realistic real-world stochastic
optimization programs, the approximation of the
underlying stochastic process describing the future
uncertainty is performed. In this work, a tree-based
discretization technique utilizing conditional
transportation distance is considered, as it is well
suited for the approximation of multi-stage
stochastic programming problems. Corresponding
convergence properties are investigated. The relation
the approximation quality of the probability model
and the quality of the solution is established.

An example of application, multistage inventory
control, is used to verify theoretical results. The
numerical solution and the obtained error bounds are
calculated explicitly.

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Radoslava Mirkov.
Radoslava Mirkov, PhD: Studies of Mathematics at the University
of Novi Sad, Serbia and at the University of Vienna, Austria.
Research assistant an the University of Vienna, currently working
at the Market Risk Management Department, Bank Austria, UniCredit
Group, Vienna, Austria.

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