Details
| Original language | English |
|---|---|
| Pages (from-to) | 409-436 |
| Number of pages | 28 |
| Journal | Computational Management Science |
| Volume | 17 |
| Issue number | 3 |
| Early online date | 5 Feb 2020 |
| Publication status | Published - Oct 2020 |
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.
Keywords
- Interior-point methods, Nonlinear stochastic optimization, Robust model predictive control, Structured inertia correction, Structured Quasi-Newton updates
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management Information Systems
- Computer Science(all)
- Information Systems
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In: Computational Management Science, Vol. 17, No. 3, 10.2020, p. 409-436.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Optimization techniques for tree-structured nonlinear problems
AU - Hübner, Jens
AU - Schmidt, Martin
AU - Steinbach, Marc C.
N1 - Funding Information: Open Access funding provided by Projekt DEAL. This research has been performed as part of the Energie Campus Nürnberg and supported by funding through the “Aufbruch Bayern (Bavaria on the move)” initiative of the state of Bavaria. Funding was provided by Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (Grant No. EnCN). Acknowledgements
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Interior-point methods
KW - Nonlinear stochastic optimization
KW - Robust model predictive control
KW - Structured inertia correction
KW - Structured Quasi-Newton updates
UR - http://www.scopus.com/inward/record.url?scp=85079500530&partnerID=8YFLogxK
U2 - 10.1007/s10287-020-00362-9
DO - 10.1007/s10287-020-00362-9
M3 - Article
AN - SCOPUS:85079500530
VL - 17
SP - 409
EP - 436
JO - Computational Management Science
JF - Computational Management Science
SN - 1619-697X
IS - 3
ER -