We investigate a structured class of nonconvex-nonconcave min-max problems exhibiting
so-called\emph {weak Minty} solutions, a notion which was only recently introduced, but is …

In the framework of real Hilbert spaces, we study continuous in time dynamics as well as
numerical algorithms for the problem of approaching the set of zeros of a single-valued …

First-order methods are often analyzed via their continuous-time models, where their worst-
case convergence properties are usually approached via Lyapunov functions. In this work …

In this work, we consider monotone Riemannian Variational Inequality Problems (RVIPs),
which encompass both Riemannian convex optimization and minimax optimization as …

Yang et al.(2023) recently showed how to use first-order gradient methods to solve general
variational inequalities (VIs) under a limiting assumption that analytic solutions of specific …

In this paper, we consider Riemannian online convex optimization with dynamic regret. First,
we propose two novel algorithms, namely the Riemannian Online Optimistic Gradient …

The recipe behind the success of deep learning has been the combination of neural
networks and gradient-based optimization. Understanding the behavior of gradient descent …

Algorithms that solve zero-sum games, multi-objective agent objectives, or, more generally,
variational inequality (VI) problems are notoriously unstable on general problems. Owing to …

We study monotone variational inequalities that can arise as optimality conditions for
constrained convex optimization or convex-concave minimax problems and propose a novel …

Minimax optimization problems have attracted a lot of attention over the past few years, with
applications ranging from economics to machine learning. While advanced optimization …