Small Gradient Norm Regret for Online Convex Optimization
arXiv:2601.13519v1 Announce Type: new
Abstract: This paper introduces a new problem-dependent regret measure for online convex optimization with smooth losses. The notion, which we call the $G^star$ regret, depends on the cumulative squared gradient norm evaluated at the decision in hindsight $sum_{t=1}^T |nabla ell(x^star)|^2$. We show that the $G^star$ regret strictly refines the existing $L^star$ (small loss) regret, and that it can be arbitrarily sharper when the losses have vanishing curvature around the hindsight decision. We establish upper and lower bounds on the $G^star$ regret and extend our results to dynamic regret and bandit settings. As a byproduct, we refine the existing convergence analysis of stochastic optimization algorithms in the interpolation regime. Some experiments validate our theoretical findings.
