Bilevel linear optimization belongs to NP and admits polynomial-size KKT-based reformulations

Fuente: arXiv
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Main Author: Buchheim, Christoph
Format: Preprint
Published: 2023
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author Buchheim, Christoph
author_facet Buchheim, Christoph
contents It is a well-known result that bilevel linear optimization is NP-hard. In many publications, reformulations as mixed-integer linear optimization problems are proposed, which suggests that the decision version of the problem belongs to NP. However, to the best of our knowledge, a rigorous proof of membership in NP has never been published, so we close this gap by reporting a simple but not entirely trivial proof. A related question is whether a large enough "big M" for the classical KKT-based reformulation can be computed efficiently, which we answer in the affirmative. In particular, our big M has polynomial encoding length in the original problem data.
format Preprint
id arxiv_https___arxiv_org_abs_2307_06639
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Bilevel linear optimization belongs to NP and admits polynomial-size KKT-based reformulations
Buchheim, Christoph
Optimization and Control
It is a well-known result that bilevel linear optimization is NP-hard. In many publications, reformulations as mixed-integer linear optimization problems are proposed, which suggests that the decision version of the problem belongs to NP. However, to the best of our knowledge, a rigorous proof of membership in NP has never been published, so we close this gap by reporting a simple but not entirely trivial proof. A related question is whether a large enough "big M" for the classical KKT-based reformulation can be computed efficiently, which we answer in the affirmative. In particular, our big M has polynomial encoding length in the original problem data.
title Bilevel linear optimization belongs to NP and admits polynomial-size KKT-based reformulations
topic Optimization and Control
url https://arxiv.org/abs/2307.06639