3 papers
math.OC2026
Exact worst-case convergence rates of gradient descent: a complete analysis for all constant stepsizes over nonconvex and convex functions
Teodor Rotaru, François Glineur, Panagiotis Patrinos
We consider gradient descent with constant stepsizes and derive exact worst-case convergence rates on the minimum gradient norm of the iterates. Our analysis covers all possible st…
math.OC2025
Tight Convergence Rates in Gradient Mapping for the Difference-of-Convex Algorithm
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We establish new theoretical convergence guarantees for the difference-of-convex algorithm (DCA), where the second function is allowed to be weakly-convex, measuring progress via c…
math.OC2025
Tight Analysis of Difference-of-Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We investigate a difference-of-convex (DC) formulation where the second term is allowed to be weakly convex. We examine the precise behavior of a single iteration of the difference…