2 papers
math.OC2026
Can Acceleration in Gradient-Norm Minimization Be Anytime? Sharp Last-Iterate Limits in Smooth Convex Optimization
Pierre Vernimmen, François Glineur
In smooth convex optimization, the gradient norm is a directly observable measure of stationarity. Accelerating a first-order method that minimizes the gradient norm is known to be…
math.OC2024
Proximal gradient methods with inexact oracle of degree q for composite optimization
Yassine Nabou, Francois Glineur, Ion Necoara
We introduce the concept of inexact first-order oracle of degree q for a possibly nonconvex and nonsmooth function, which naturally appears in the context of approximate gradient,…