2 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Improved convergence rates for the Difference-of-Convex algorithm
Teodor Rotaru, Panagiotis Patrinos, François Glineur
We consider a difference-of-convex formulation where one of the terms is allowed to be hypoconvex (or weakly convex). We first examine the precise behavior of a single iteration of…
Tight convergence rates of the gradient method on smooth hypoconvex functions
Teodor Rotaru, François Glineur, Panagiotis Patrinos
We perform the first tight convergence analysis of the gradient method with varying step sizes when applied to smooth hypoconvex (weakly convex) functions. Hypoconvex functions are…