Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288275 
Year of Publication: 
2020
Citation: 
[Journal:] Computational Management Science [ISSN:] 1619-6988 [Volume:] 17 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 409-436
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Robust model predictive control approaches and other applications lead to nonlinear optimization problems defined on (scenario) trees. We present structure-preserving Quasi-Newton update formulas as well as structured inertia correction techniques that allow to solve these problems by interior-point methods with specialized KKT solvers for tree-structured optimization problems. The same type of KKT solvers could be used in active-set based SQP methods. The viability of our approach is demonstrated by two robust control problems.
Subjects: 
Nonlinear stochastic optimization
Interior-point methods
Structured Quasi-Newton updates
Structured inertia correction
Robust model predictive control
JEL: 
C06
C15
C30
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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