Kleinert T (2023)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2023
Publisher: Springer Nature
Edited Volumes: Operations Research Proceedings 2022
Series: Lecture Notes in Operations Research
Book Volume: Part F3789
Pages Range: 11-17
DOI: 10.1007/978-3-031-24907-5_2
In this article, we summarize a subset of the findings of the cumulative dissertation “Algorithms for Mixed-Integer Bilevel Problems with Convex Followers”; see [4]. First, we present a result that renders the application of the well-known and widely used big-M reformulation of linear bilevel problems infeasible for many practical applications. Second, we present valid inequalities and demonstrate that an SOS1-based approach is a competitive alternative to the error-prone big-M method in case both approaches are equipped with these valid inequalities. Third, we introduce a penalty alternating direction method, which computes (close-to-)optimal feasible points in extremely short computation times and outperforms a state-of-the-art local method.
APA:
Kleinert, T. (2023). Computational Linear Bilevel Optimization. In Oliver Grothe, Stefan Nickel, Steffen Rebennack, Oliver Stein (Eds.), Operations Research Proceedings 2022. (pp. 11-17). Springer Nature.
MLA:
Kleinert, Thomas. "Computational Linear Bilevel Optimization." Operations Research Proceedings 2022. Ed. Oliver Grothe, Stefan Nickel, Steffen Rebennack, Oliver Stein, Springer Nature, 2023. 11-17.
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