13 citations · 16 across the 4 of their papers we have counts for
10 papers · 1 filter
Mini-batch stochastic three-operator splitting for distributed optimization
Barbara Franci, Mathias Staudigl
We consider a network of agents, each with its own private cost consisting of a sum of two possibly nonsmooth convex functions, one of which is composed with a linear operator. At…
A relaxed-inertial forward-backward-forward algorithm for Stochastic Generalized Nash equilibrium seeking
Shisheng Cui, Barbara Franci, Sergio Grammatico +2
In this paper we propose a new operator splitting algorithm for distributed Nash equilibrium seeking under stochastic uncertainty, featuring relaxation and inertial effects. Our wo…
First-Order Methods for Convex Optimization
Pavel Dvurechensky, Mathias Staudigl, Shimrit Shtern
First-order methods for solving convex optimization problems have been at the forefront of mathematical optimization in the last 20 years. The rapid development of this important c…
Generalized Self-Concordant Analysis of Frank-Wolfe algorithms
Pavel Dvurechensky, Kamil Safin, Shimrit Shtern +1
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in large scale optimization for machine learning and computati…
Self-Concordant Analysis of Frank-Wolfe Algorithms
Pavel Dvurechensky, Petr Ostroukhov, Kamil Safin +2
Projection-free optimization via different variants of the Frank-Wolfe (FW), a.k.a. Conditional Gradient method has become one of the cornerstones in optimization for machine learn…
Inducing strong convergence of trajectories in dynamical systems associated to monotone inclusions with composite structure
Radu Ioan Boţ, Sorin-Mihai Grad, Dennis Meier +1
In this work we investigate dynamical systems designed to approach the solution sets of inclusion problems involving the sum of two maximally monotone operators. Our aim is to desi…