259 citations
- Centre de Recherche en Économie et StatistiqueFR18 papers
- Centre Inria de SaclayFR16 papers
- Centre National de la Recherche ScientifiqueFR14 papers
- Sorbonne UniversitéFR14 papers
- Laboratoire d'Informatique de GrenobleFR12 papers
- École Normale Supérieure Paris-SaclayFR11 papers
- ENSAE ParisFR11 papers
- École Nationale d'AdministrationSN9 papers
- Laboratoire d'Informatique, du Traitement de l'Information et des SystèmesFR8 papers
- École PolytechniqueFR7 papers
- Institut Systèmes Intelligents et de RobotiqueFR7 papers
- POLARIS: Évaluation et optimisation des performances des grandes infrastructuresFR6 papers
9 papers · 1 filter
Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements
Kimon Antonakopoulos, Panayotis Mertikopoulos
We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth in the standard sense. Specif…
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
Waïss Azizian, Franck Iutzeler, Jérôme Malick +1
In this paper, we analyze the local convergence rate of optimistic mirror descent methods in stochastic variational inequalities, a class of optimization problems with important ap…
Decomposition-Coordination Method for Finite Horizon Bandit Problems
Michel de Lara, Benjamin Heymann, Jean-Philippe Chancelier
Optimally solving a multi-armed bandit problem suffers the curse of dimensionality. Indeed, resorting to dynamic programming leads to an exponential growth of computing time, as th…
Kuhn's Equivalence Theorem for Games in Intrinsic Form
Benjamin Heymann, Michel de Lara, Jean-Philippe Chancelier
We state and prove Kuhn's equivalence theorem for a new representation of games, the intrinsic form. First, we introduce games in intrinsic form where information is represented by…
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis +1
This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems. We first show that the s…
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher
Compared to ordinary function minimization problems, min-max optimization algorithms encounter far greater challenges because of the existence of periodic cycles and similar phenom…