On the Hypomonotone Class of Variational Inequalities
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Abstract (arXiv)
This paper studies the behavior of the extragradient algorithm [Korpelevich, 1976] when applied to hypomonotone operators, a class of problems that extends beyond the classical monotone setting. To support the understanding of this variational inequality problem class, we focus on a subclass of hypomonotone linear operators, characterizing them based on their eigenvalues and providing concrete examples. While the extragradient method is widely recognized for its efficiency in solving variational inequalities involving monotone and Lipschitz continuous operators, we demonstrate that it does not guarantee convergence in the hypomonotone case. In particular, we construct a counterexample where the extragradient method diverges regardless of the step size. A numerical experiment is presented to support this result.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 11 Oct 2024.
Builds on
5 of 13 references.
References · 5
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2013Intriguing properties of neural networkscited by 5,114
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
- 2020A causal framework for distribution generalizationcited by 68
- 2020Minimax Classification with 0-1 Loss and Performance Guaranteescited by 11
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Citing papers per year: 2026: 1.
1 paper.
- 2026On the Computational Complexity of Performative Prediction28 Jan 2026