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Journal Articles Operations Research Year : 2020

There's No Free Lunch: On the Hardness of Choosing a Correct Big-M in Bilevel Optimization

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Abstract

One of the most frequently used approaches to solve linear bilevel optimization problems consists in replacing the lower-level problem with its Karush-Kuhn-Tucker (KKT) conditions and by reformulating the KKT complementarity conditions using techniques from mixed-integer linear optimization. The latter step requires to determine some big-M constant in order to bound the lower level's dual feasible set such that no bilevel-optimal solution is cut off. In practice, heuristics are often used to find a big-M although it is known that these approaches may fail. In this paper, we consider the hardness of two proxies for the above mentioned concept of a bilevel-correct big-M. First, we prove that verifying that a given big-M does not cut off any feasible vertex of the lower level's dual polyhedron cannot be done in polynomial time unless P = NP. Second, we show that verifying that a given big-M does not cut off any optimal point of the lower level's dual problem (for any point in the projection of the high-point relaxation onto the leader's decision space) is as hard as solving the original bilevel problem.
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Dates and versions

hal-03137942 , version 1 (10-02-2021)

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Thomas Kleinert, Martine Labbé, Fränk Plein, Martin Schmidt. There's No Free Lunch: On the Hardness of Choosing a Correct Big-M in Bilevel Optimization. Operations Research, 2020, 68 (6), ⟨10.1287/opre.2019.1944⟩. ⟨hal-03137942⟩
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