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On constraint qualifications for MPECs with applications to bilevel hyperparameter optimization for machine learning

On constraint qualifications for MPECs with applications to bilevel hyperparameter optimization for machine learning
On constraint qualifications for MPECs with applications to bilevel hyperparameter optimization for machine learning
Constraint qualifications for a Mathematical Program with Equilibrium Constraints (MPEC) are essential for analyzing stationarity properties and establishing convergence results. In this paper, we explore several classical MPEC constraint qualifications and clarify the relationships among them. We subsequently examine the behavior of these constraint qualifications in the context of a specific MPEC derived from bilevel hyperparameter optimization (BHO) for L1-loss support vector classification. In particular, for such an MPEC, we provide a complete characterization of the well-known MPEC linear independence constraint qualification (MPEC-LICQ), therefore, establishing conditions under which it holds or fails for our BHO for support vector machines.
math.OC
2331-8422
Li, Jiani
87862482-8898-46f8-a246-a0d112ca23a4
Li, Qingna
a189d836-f8f0-407b-9983-0a73bf8a214a
Zemkoho, Alain
30c79e30-9879-48bd-8d0b-e2fbbc01269e
Li, Jiani
87862482-8898-46f8-a246-a0d112ca23a4
Li, Qingna
a189d836-f8f0-407b-9983-0a73bf8a214a
Zemkoho, Alain
30c79e30-9879-48bd-8d0b-e2fbbc01269e

Li, Jiani, Li, Qingna and Zemkoho, Alain (2025) On constraint qualifications for MPECs with applications to bilevel hyperparameter optimization for machine learning. arXiv. (In Press)

Record type: Article

Abstract

Constraint qualifications for a Mathematical Program with Equilibrium Constraints (MPEC) are essential for analyzing stationarity properties and establishing convergence results. In this paper, we explore several classical MPEC constraint qualifications and clarify the relationships among them. We subsequently examine the behavior of these constraint qualifications in the context of a specific MPEC derived from bilevel hyperparameter optimization (BHO) for L1-loss support vector classification. In particular, for such an MPEC, we provide a complete characterization of the well-known MPEC linear independence constraint qualification (MPEC-LICQ), therefore, establishing conditions under which it holds or fails for our BHO for support vector machines.

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2508.12850v1 - Accepted Manuscript
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Accepted/In Press date: 18 August 2025
Keywords: math.OC

Identifiers

Local EPrints ID: 508837
URI: http://eprints.soton.ac.uk/id/eprint/508837
ISSN: 2331-8422
PURE UUID: a7ad95e4-8878-4ee2-98ce-62208ea39987
ORCID for Alain Zemkoho: ORCID iD orcid.org/0000-0003-1265-4178

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Date deposited: 04 Feb 2026 17:45
Last modified: 05 Feb 2026 02:47

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Contributors

Author: Jiani Li
Author: Qingna Li
Author: Alain Zemkoho ORCID iD

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