Journal of Optimization Theory and Applications

, Volume 136, Issue 2, pp 275–295 | Cite as

Adjustable Robust Optimization Models for a Nonlinear Two-Period System



We study two-period nonlinear optimization problems whose parameters are uncertain. We assume that uncertain parameters are revealed in stages and model them using the adjustable robust optimization approach. For problems with polytopic uncertainty, we show that quasiconvexity of the optimal value function of certain subproblems is sufficient for the reducibility of the resulting robust optimization problem to a single-level deterministic problem. We relate this sufficient condition to the cone-quasiconvexity of the feasible set mapping for adjustable variables and present several examples and applications satisfying these conditions.


Robust optimization Two-period nonlinear optimization problem Quasiconvex set valued map 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Mathematical and Computing SciencesTokyo Institute of TechnologyTokyoJapan
  2. 2.Digital Media Network CompanyToshiba CorporationTokyoJapan
  3. 3.Quantitative Investment StrategiesGoldman Sachs Asset ManagementNew YorkUSA

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