13 citations · 16 across the 4 of their papers we have counts for
12 papers
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…
A competitive search game with a moving target
Benoit Duvocelle, János Flesch, Mathias Staudigl +1
We introduce a discrete-time search game, in which two players compete to find an object first. The object moves according to a time-varying Markov chain on finitely many states. T…
Incentive compatibility in sender-receiver stopping games
Aditya Aradhye, János Flesch, Mathias Staudigl +1
We introduce a model of sender-receiver stopping games, where the state of the world follows an iid--process throughout the game. At each period, the sender observes the current st…