36 citations · 98 across the 20 of their papers we have counts for
25 papers
A regularized variance-reduced modified extragradient method for stochastic hierarchical games
Shisheng Cui, Uday V. Shanbhag, Mathias Staudigl
We consider an N-player hierarchical game in which the i-th player's objective comprises of an expectation-valued term, parametrized by rival decisions, and a hierarchical term. Su…
Random block coordinate methods for inconsistent convex optimisation problems
Mathias Staudigl, Paulin Jacquot
We develop a novel randomised block coordinate primal-dual algorithm for a class of non-smooth ill-posed convex programs. Lying in the midway between the celebrated Chambolle-Pock…
A conditional gradient homotopy method with applications to Semidefinite Programming
Pavel Dvurechensky, Gabriele Iommazzo, Shimrit Shtern +1
We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints. Instances of this template nat…
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…
Proximal-like algorithms for equilibrium seeking in mixed-integer Nash equilibrium problems
Filippo Fabiani, Barbara Franci, Simone Sagratella +2
We consider potential games with mixed-integer variables, for which we propose two distributed, proximal-like equilibrium seeking algorithms. Specifically, we focus on two scenario…
Hessian barrier algorithms for non-convex conic optimization
Pavel Dvurechensky, Mathias Staudigl
We consider the minimization of a continuous function over the intersection of a regular cone with an affine set via a new class of adaptive first- and second-order optimization me…
Stochastic Relaxed Inertial Forward-Backward-Forward splitting for Monotone Inclusions in Hilbert spaces
Shisheng Cui, Uday V. Shanbhag, Mathias Staudigl +1
We consider monotone inclusions defined on a Hilbert space where the operator is given by the sum of a maximal monotone operator and a single-valued monotone, Lipschitz continu…
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…
A privacy-preserving distributed computational approach for distributed locational marginal prices
Olivier Bilenne, Paulin Jacquot, Nadia Oudjane +3
An important issue in today's electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles. By inducing chang…
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…
Computing Dynamic User Equilibrium on Large-Scale Networks Without Knowing Global Parameters
Duong Viet Thong, Aviv Gibali, Mathias Staudigl +1
Dynamic user equilibrium (DUE) is a Nash-like solution concept describing an equilibrium in dynamic traffic systems over a fixed planning period. DUE is a challenging class of equi…
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…
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…
Generalized Self-concordant Hessian-barrier algorithms
Pavel Dvurechensky, Mathias Staudigl, César A. Uribe
Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible s…
Distributed forward-backward (half) forward algorithms for generalized Nash equilibrium seeking
Barbara Franci, Mathias Staudigl, Sergio Grammatico
We present two distributed algorithms for the computation of a generalized Nash equilibrium in monotone games. The first algorithm follows from a forward-backward-forward operator…
Strong Convergence of Forward-Backward-Forward Methods for Pseudo-monotone Variational Inequalities with Applications to Dynamic User Equilibrium in Traffic Networks
Benoit Duvocelle, Dennis Meier, Mathias Staudigl +1
In infinite-dimensional Hilbert spaces we device a class of strongly convergent primal-dual schemes for solving variational inequalities defined by a Lipschitz continuous and pseud…
Forward-backward-forward methods with variance reduction for stochastic variational inequalities
Radu Ioan Bot, Panayotis Mertikopoulos, Mathias Staudigl +1
We develop a new stochastic algorithm with variance reduction for solving pseudo-monotone stochastic variational inequalities. Our method builds on Tseng's forward-backward-forward…
Hessian barrier algorithms for linearly constrained optimization problems
Immanuel M. Bomze, Panayotis Mertikopoulos, Werner Schachinger +1
In this paper, we propose an interior-point method for linearly constrained optimization problems (possibly nonconvex). The method - which we call the Hessian barrier algorithm (HB…
Multi-agent online learning in time-varying games
Benoit Duvocelle, Panayotis Mertikopoulos, Mathias Staudigl +1
We examine the long-run behavior of multi-agent online learning in games that evolve over time. Specifically, we focus on a wide class of policies based on mirror descent, and we s…
Stochastic mirror descent dynamics and their convergence in monotone variational inequalities
Panayotis Mertikopoulos, Mathias Staudigl
We examine a class of stochastic mirror descent dynamics in the context of monotone variational inequalities (including Nash equilibrium and saddle-point problems). The dynamics un…
On the convergence of gradient-like flows with noisy gradient input
Panayotis Mertikopoulos, Mathias Staudigl
In view of solving convex optimization problems with noisy gradient input, we analyze the asymptotic behavior of gradient-like flows under stochastic disturbances. Specifically, we…