  Algorithms for min-max optimization and variational inequalities are often
studied under monotonicity assumptions. Motivated by non-monotone machine
learning applications, we follow the line of works [Diakonikolas et al., 2021,
Lee and Kim, 2021, Pethick et al., 2022, B\"ohm, 2022] aiming at going beyond
monotonicity by considering the weaker negative comonotonicity assumption. In
particular, we provide tight complexity analyses for the Proximal Point,
Extragradient, and Optimistic Gradient methods in this setup, closing some
questions on their working guarantees beyond monotonicity.
