3 citations · 3 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
Random block coordinate descent methods for linearly constrained optimization over networks
I. Necoara, Yu. Nesterov, F. Glineur
In this paper we develop random block coordinate gradient descent methods for minimizing large scale linearly constrained separable convex problems over networks. Since we have cou…