Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315457 
Year of Publication: 
2024
Citation: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 18 [Issue:] 5 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2024 [Pages:] 1303-1311
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We consider adjustable robust linear complementarity problems and extend the results of Biefel et al. (SIAM J Optim 32:152–172, 2022) towards convex and compact uncertainty sets. Moreover, for the case of polyhedral uncertainty sets, we prove that computing an adjustable robust solution of a given linear complementarity problem is equivalent to solving a properly chosen mixed-integer linear feasibility problem.
Subjects: 
Linear complementarity problems
Adjustable robustness
Robust optimization
Mixed-integer linear optimization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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