Why there is no need to use a big-M in linear bilevel optimization: a computational study of two ready-to-use approaches
Kleinert T, Schmidt M (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 20
Article Number: 3
Journal Issue: 1
DOI: 10.1007/s10287-023-00435-5
Abstract
Linear bilevel optimization problems have gained increasing attention both in theory as well as in practical applications of Operations Research (OR) during the last years and decades. The latter is mainly due to the ability of this class of problems to model hierarchical decision processes. However, this ability makes bilevel problems also very hard to solve. Since no general-purpose solvers are available, a “best-practice” has developed in the applied OR community, in which not all people want to develop tailored algorithms but “just use” bilevel optimization as a modeling tool for practice. This best-practice is the big-M reformulation of the Karush–Kuhn–Tucker (KKT) conditions of the lower-level problem—an approach that has been shown to be highly problematic by Pineda and Morales (2019). Choosing invalid values for M yields solutions that may be arbitrarily bad. Checking the validity of the big-Ms is however shown to be as hard as solving the original bilevel problem in Kleinert et al. (2019). Nevertheless, due to its appealing simplicity, especially w.r.t. the required implementation effort, this ready-to-use approach still is the most popular method. Until now, there has been a lack of approaches that are competitive both in terms of implementation effort and computational cost. In this note we demonstrate that there is indeed another competitive ready-to-use approach: If the SOS-1 technique is applied to the KKT complementarity conditions, adding the simple additional root-node inequality developed by Kleinert et al. (2020) leads to a competitive performance—without having all the possible theoretical disadvantages of the big-M approach.
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APA:
Kleinert, T., & Schmidt, M. (2023). Why there is no need to use a big-M in linear bilevel optimization: a computational study of two ready-to-use approaches. Computational Management Science, 20(1). https://doi.org/10.1007/s10287-023-00435-5
MLA:
Kleinert, Thomas, and Martin Schmidt. "Why there is no need to use a big-M in linear bilevel optimization: a computational study of two ready-to-use approaches." Computational Management Science 20.1 (2023).
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